What is loan origination?

Loan origination is all the steps your operation makes prior to disbursing the loan. And, arguably, that’s the stage of the lending process that takes the most attention and effort. Because without an intelligent and easy-to-use digital origination, a business won’t make it in the socially distanced society. Loan origination cycle you use determines the selection of the borrowers, their evaluation, filtering, terms selection, and, consequently, how well your business performs. 

Borrowers want to get their financing where they need it, when they need it, and how they want it. At the same time, setting up the best possible digital processes for your borrowers from the first try is no easy task, especially if at the same time you need to be extremely cautious of bad debt and unreliable clients. 

Loan Origination Process Steps

Having automated lending processes in 50+ countries and pioneering the intelligent digital revolution of lending, we know the struggles business owners face most often when setting up an origination process. That’s why we put together a 10-step process to launch a truly automated digital loan origination process for your lending business. The process that will protect you and delight your borrowers. 

STEP 1: SELECTING YOUR LOAN ORIGINATION AUTOMATION SYSTEM 

In recent years, FinTech, specifically loan origination systems and lending automation solutions, has drastically lowered the lending industry entry barrier, making bank-grade automation accessible and affordable to SME and large lenders willing to digitize their crediting processes. So before you start putting together new business processes for your lending operation, look into what the technology is capable of doing for you. 

For example, most banks still take as much as nine business days to analyze a loan application, evaluate risks, make a crediting decision, and then either disburse funds or decline the application. For any modern borrower, who’s used to getting an Uber in minutes, that’s stone age. At the same time, when switching to an automated system, like the Loan Origination System by TurnKey Lender, you’ll be able to complete the entire origination process (including disbursement) within minutes. 

We apply proprietary deep neural networks and machine learning algorithms to collect relevant data and analyze it within seconds. This significantly reduces origination process time, eliminates human error, and leads to costs reduction by automating recurring tasks. 

Based on our experience in North America and worldwide, the universal key steps to choosing the best loan origination and lending automation software for your business include a thorough analysis of the following:

• Define your short and long-term business needs and what you need to accomplish with this new digital system
• Consider the benefits of an all-in-one system with a modular structure
• Research cloud-based vs on-premises software to see which is best for your business needs
• Make sure to get comprehensive, integrated functionality for all parts
• Check for smart automation with machine learning and proprietary credit scoring
• Consider user-friendliness for your customers
• Make sure there are country-specific editions of the platform
• Review the ease of business logic customization
• Make sure the time to market is fast and it is easy to deploy and easy to learn
• Review the providers’ proven track record with businesses similar to your own

Another reason, why the choice of the loan origination system is a big one, is because migrating from the wrong one will take a ton of time and resources.  If you are looking to learn more on this, you can read our in-depth guide devoted to choosing a lending automation solution for your business. 

And if you look beyond origination, a unified lending management solution would include loan origination as well as servicing, collection, reporting, integrations, and more, so once you’re done reading that article, there is a free white paper at the end with further best practices on choosing the best digital loan origination system for your business.

Plus, we’ve recently published a brand-new article devoted specifically to choosing the right origination software provider for your business. Feel free to check it out here and let us know if you have any additional questions:

Seven Ways to Tell if an LOS Provider is Right for You

STEP 2: LOAN ORIGINATION PROCESS REVIEW

Having researched and selected the loan origination system, you’ll see that based on its capabilities you have a pretty clear idea of the origination process you can implement with it. Now you need to put it on paper and analyze your business needs, borrower’s preferred channels, risks, and unique selling propositions. Not to mention, the origination process of your local competitors and the international market’s top performers. Because in the global world, if some technology is accessible to one company, chances are, it can be replicated and improved upon, no matter where you work. 

And while the business model and logic are paramount, it’s extremely important to consult with a local regulations specialist to go through the ins-and-outs of the state’s requirements and avoid stepping on some regulatory land mine. 

Once the process is set from the compliance standpoint, review it with your team, and once again go over the system you chose with your staff. You want input from people on different stages of your lending process involved in the discussion because a hands-on originator will be able to provide you with easy-to-miss details and you will come to more well-rounded decisions.

STEP 3: LOAN ORIGINATION SOLUTION CONFIGURATION

Depending on the loan origination solution you choose, the time-to-market and customization options will differ. An advanced FinTech may be ready-to-use out of the box and deployed within days, but just as well you can get tangled in a confusing system that would take you and your team months before you actually start applying it to your loan origination process. 

With the right software provider, you won’t need to do much other than control the quality and completeness of the customization and decide when you want to deploy. If you’re a small- to mid-size retailer, alternative lender, or just looking to offer in-house financing, chances are your lending automation needs will be more or less typical for the industry. 

If that’s the case, the completely operational end-to-end solution by TurnKey Lender can be deployed for your business within a day of signing the contract. If you want to customize the solution it can take a little more time, however, Turnkey Lender has the most robust solution with the fastest time-to-market. You get a flexible and scalable platform that our team can adjust to any of your business needs. The time-to-market will differ by packages, so you can choose which fits your business best:

• Separate configurable modules for loan origination, servicing, underwriting, collection, and more 
• A ready-to-use boxed solution for end-to-end automation
• An Transformer solution to address your unique needs and meet the requirements of a large-scale institution

You can email our team at sales@turnkey-lender.com if you’d like to discuss your specific project and see what we can do for you.

STEP 4: THE ROLE OF PREQUALIFICATION IN THE DIGITAL LENDING PROCESS

The first point of contact lenders get with potential borrowers is prequalification. Once your digital loan origination solution is up-and-running and you have received a lead you need to request the personal information you need for AML and KYC compliance or analyze the info the lead submitted from a form on your website. 

So in order for the prequalification to work smoothly, the loan origination software needs to have a flexible loan application form settings to collect and process the data that will actually help make an informed credit decision.  Some of the data points you may need to collect include:

• Legal name (including maiden or middle names if applicable)
• Permanent address
• Telephone number and email
• Date and place of birth
• Sex, marital status
• Nationality
• Occupation
• Position held and/or name of employer
• An official personal identification number or other unique identifier contained in an official document with a photo (e.g. passport, identification card, residence permit, social security records, driving license)
• Type of bank account and income sources
• Signature

Note:

All that, including the signature, can be collected without ever visiting a branch with an e-signature service integration.

Check out an in-depth article about borrower identification in digital lending here.

STEP 5: ONLINE APPLICATION AND APPLICATION PROCESSING

To transition to an e-lending crediting model you may want to consider to keep both doors open to your customers at first: let them fill out a paper form (you can have your employees type the info into the solution directly) and also give them the option to apply online without even coming to the company or branch. 

Based on the extensive experience TurnKey Lender’s customers have with this approach, it’s the optimal way that doesn’t create stress for the more traditional customers and meets the needs of customers who prefer to do things digitally. The only part that really matters here is that you collect the data needed to pass to processing and underwriting.

With the right loan origination solution, you won’t have trouble processing many more loans than before. The problem is that not all loan origination solutions apply intelligent borrower evaluation approaches for thorough analysis of borrower evaluation and risk management. This is why the provider you select is of critical importance.

TurnKey Lender uses deep neural networks to get as much insight from the customer data as possible and help you approve more of the right loans in a matter of seconds. 

STEP 6: UNDERWRITING

Once data collection and application processing are taken care of, you can get to the fun part. And by fun, we mean the hardest thing lenders deal with in the world of digital lending – analyzing the borrower data, and making the right credit decision based on it. With the traditional borrower evaluation approaches becoming obsolete, lenders need to strongly consider fully digital, automated underwriting approaches for gauging risks quickly and accurately.

Usually, underwriting consists of multiple levels of borrower’s data analysis, risk scoring, and evaluations. To improve credit decisioning accuracy, you can apply several scoring models to every application. With an advanced FinTech like TurnKey Lender, this doesn’t lead to any added expenses and comes as built-in functionality. In a nutshell, you provide the system with your own set of decision rules and adjust the scorecard setting to correctly evaluate the data points you receive. 

TurnKey Lender provides you with a system powered by deep neural networks with self-learning scoring models for both traditional and alternative evaluation approaches and data sources. Working with the client data, the system learns to use prediction, classification, clustering, and association in application processing. For safety purposes, the system doesn’t just use the data client is providing but also pulls the available information from the databases it’s synchronized with (like the credit bureaus). All the data is processed by the TurnKey Lender’s algorithms and is then presented in the form of a risk evaluation. 

Even though the credit decisioning that comes built-in with TurnKey Lender presents an excellent usage of these advanced technologies on its own, the team didn’t stop there. The algorithms and models are polished and upgraded to take into account more factors and learn faster with each new release. The experience the team got working with clients from all over the world led us to create a new, standalone, product called TurnKey Lender Psychometrics. 

It’s an app that uses the original AI-powered decisioning engine as a starting point and enhances it through a psychological test that was put together by experienced psychologists, lending specialists, and in-house AI engineers. The test, combined with the deep neural networks which analyze its results, allows to accurately evaluate loan risks and potential borrowers even in cases when there’s no access to their credit history or even bank accounts. 

The completion of a test by a potential borrower and risk evaluation can take as little as 6 minutes. As a result, the lender gets a risk score and they can make an informed loan decision based on deep analysis of the user’s psychological profile and behavior rather than lose business or run high risks.

That’s just one of the examples of how alternative credit scoring can be used to improve decisioning accuracy. The vast majority of our clients are fully satisfied with the built-in scoring models and decision rules, but should your business need unique underwriting, we can easily tailor the platform to your technical requirements.  

STEP 7: INTELLIGENT CREDIT DECISIONING 

The key benefit of lending business automation, other than convenience, is the data you’re able you aggregate, process, analyze, and get insights from. Big data and AI go hand-in-hand since it takes self-learning algorithms to get through the tangles of data points and make sense of it. 

With the rise of big data, credit decisioning gets a lot more granular and accurate. So the lack of data for analysis is not the problem. The hard part is to get a solution sophisticated enough to get all the needed insights from this data in a matter of seconds. 

TurnKey Lender applies artificial intelligence to carry out risk evaluation and borrower evaluation for your business. You can customize the credit scorecard and watch your rules take seconds to be executed in a borrower’s evaluation. TurnKey Lender’s proprietary algorithms study the applications which are more successful and tweak the evaluation algorithms to grant you even higher decision accuracy. 

Decision Management System

A human would take days to run all the checks on this data and a traditional machine isn’t much faster, but proprietary self-learning machine learning algorithms created by TurnKey Lender does this in seconds. The interest rate assigned to each application depends on the risk factors connected to each borrower, taking into account not just data about their past but understanding their psychological portrait and likelihood to pay back what they owe. 

STEP 8: QUALITY CONTROL 

Right now is the new industrial revolution and the businesses that embrace automation will stay competitive in the world to come. The good news is that to scale a lending operation in the digital age you don’t need dozens of local branches with rent and staff on the payroll in all of them. Lending technology in its current state is perfectly capable of automating the vast majority of lending business’ operations, freeing up human resources and drastically improving speed and performance. 

And even though you may not feel comfortable delegating credit decisions and risk evaluation to machines at first, what you may want to do is do it semi-automatically at first, then switch to checking only the declined application and when you see that the errors evaporated, simply make ad-hoc checks from time to time. 

The goal of the lender at this stage of loan origination is to involve as little human resources as possible, first and foremost, cause it proves incomparable in environments where instant analysis of millions of data points is required. 

An important thing to keep in mind is that on the stage of quality control, you take on responsibility for each borrower in the eyes of the regulators. So there should be people who analyze the applications from time to time, but it’s extremely important that the process is at least semi-automatic, because the time to funding is most important to your success in today’s and tomorrow’s markets.

STEP 9: FUNDS DISBURSEMENT

In this theoretical digital lending business flow, we’ve already collected the borrower’s data, conducted all the origination activities, and had the application approved by an underwriter. Once you’ve made it this far, disbursement is the easy part. 

Any digital lending automation system worth its salt integrates with payment software and will automatically send the money to the borrower once all the checks on your end are completed and the loan is approved for disbursement. The same goes for debt collection. 

From there, a good loan can basically live on its own with the borrower making scheduled automatic repayments, fees, and interest, as well as collecting all the data that will, later on, help you prove you did your due diligence.

STEP 10: TRACKING AND REPORTING

Once the funds are out of the lender’s pocket, loan origination ends. However, it is of utmost importance that all the data and borrower information is formatted, stored safely, and passed into a synchronized reporting software. This will help with regulatory compliance, cut operational costs, help find inefficiencies, and eliminate human error. In order to achieve this, the tracking and reporting modules should be fully integrated into the loan origination software. 

IN CONCLUSION

If you don’t hit the mark during the loan origination, a good borrower isn’t likely to come back but the bad ones will come in mass. Credit scoring, automation, evaluation, integrations all take time and resources. But with the technology we have today, the software does all the heavy lifting. So if you were considering launching a digital lending operation, there’s really nothing stopping you. Here are all the steps in an infographic, just in case:

And don’t hesitate to contact our team with any questions or requests!

TurnKey Lender provides lenders with bank-grade technology that automates every step of the lending process at a fraction of the price and time-to-market. The loan origination module of the TurnKey Lender solution covers every one of the 10 steps listed above and can also be used as a part of the end-to-end solution. Let’s chat. 

Consumers choose speed and convenience over price, even when it comes to their finances. That’s why successful lenders strive to deliver instant approvals, and 1-day funds transfers. They’re training borrowers to expect quick action with every application. Can your lending operation approve loans at warp speed, without sacrificing credit quality?

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According to a PACE Insights report, borrowers say they’re frustrated with an application review process that crawls along, compared to other purchase and transactional experiences. They go on to say that their top three decision criteria when choosing a lender are speed, convenience, and reliability. Did you notice that speed is at the top of the list, and low price isn’t even on the list? This could prove to be a good challenge for the lending community. 

Borrower needs and wants

When a consumer needs extra cash, or a business needs more working capital, they become credit hungry. This term is not an exaggeration. These potential borrowers can sometimes feel physical discomfort until their financial needs have been resolved, which explains why they focus on speed instead of price. They’ll close with the lender who delivers the fastest approval, not the lowest interest rate. 

In our hyper-competitive marketplace, one of the most important predictors of lender performance is time-to-funding. This is the time between application submission and access to funds. Successful lenders consistently deliver approvals in hours, and funds within a few days. In fact, several digital lenders promote their products, by promising 1-day and 2-day time-to-funding in their advertising headlines. 

How do lenders adjust to this new norm, where time-to-funding has been reduced to a single day? You could throw more people at the problem, but more hands won’t speed up the process. The only way to deliver account approvals at this pace is to automate your application review process with technology platforms, automated systems software, and advanced credit scoring software. 

It may sound like a big investment, but these FinTech systems deliver several benefits that work to improve portfolio profitability:

• approve applications faster
• create process efficiencies
• reduce manpower
• lower operating costs
• increase the number of booked accounts
• decrease the cost per booked account
• improve risk profile
• improve portfolio yield. 

Now may be a good time to invest in a cost / benefit analysis that will quantify the impact of automation technology and advanced credit scoring software on your loan portfolio. 

Balancing speed with credit risk 

The primary goal for every lender is to maximize their portfolio yield. So it’s important to understand how small changes to the origination or account management processes could potentially increase the risk profile and reduce returns. 

Historically, lenders have equated decision speed with credit risk. The faster the approval, the higher the risk. That’s because they were gaining time by cutting corners. Credit decision software has changed this equation. Today’s lenders can control credit risk, without slowing down the process, by leveraging a fully managed LaaS platform. 

The TurnKey Lender platform leverages traditional and non-traditional credit scoring combined with machine learning to constantly refine the credit scorecard. Our clients enjoy a faster approval process that delivers more new accounts, all accurately priced to maintain and improve portfolio yield. 

Best practices for faster applications processing

Let’s look at some of the ways lenders pick up the pace when it comes to the application approval process. At TurnKey Lender we monitor best practices used by digital lenders, alternative funders, and online banks. In addition, we like to keep an eye on the e-commerce industry. This group of online sellers is on the leading edge of conversion rate optimization (CRO). And quite a few of their conversion rate techniques are applicable to the lending industry.

Attract the right prospects

Start by reviewing your prospecting strategy, search engine optimization (SEO) tactics, and advertising messages. You want to make sure you’re targeting the prospect audience that is searching for the type of loan you offer, and is likely to pass through your credit screens. Even the most sophisticated lending platform won’t be able to convert the wrong target audience.

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Optimize your digital processes 

Akamai Internet Observatory published a study showing the negative impact of slow technology: 

• 79% of participants hesitate to complete a transaction on a lackluster website.
• 50% click to a competitor’s website when load-time exceeds 3.0 seconds. 

We believe these behaviors are due to subconscious assumptions made by consumers. They translate cutting edge functionality to cutting edge cybersecurity, cutting edge products, and cutting edge customer service. On the other hand, dated functionality and slow manual processes makes them question the quality of all three.

On the positive side e-commerce websites increased their completed transactions by 7%-12% when load-time was reduced by 1.0 second. 

It’s a good idea to audit your system on a regular basis. You want to make sure every individual link in the application path is optimized and cybersecure. 

The digital processes checklist should include:

• traffic driving ads and blog posts 
• landing pages
• website pages
• application forms
• call center systems
• onboarding processes
• account communications.

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Optimize the application path

Work with user experience (UX) professionals to cut down the borrower path to the minimum number of steps, and the minimum amount of information you need to make a good credit decision. Audit the path and reduce any friction. It’s the enemy of speed. Any extraneous steps, or nice to have information fields, will slow down the process and lose good accounts to the competition. There will be plenty of opportunity to capture additional borrower information, to support upsell and cross-sell campaigns, as you nurture a relationship with your new account holder.

The application path checklist should include:

• Minimize the number of steps in the process, and the number of information fields on a form. 
• Implement an omni-channel system that integrates desktop and mobile devices, so your prospect can start an application on one device and complete the application on another device. 
• Include red box tactics that force applicants to complete every field before moving forward. 
• Implement an auto-save function to make the process easier for an applicant who starts an application, and returns later to complete the form.
• Integrate the account approval process with your onboarding process for a seamless borrower experience. 
• Incorporate a debit card into your product features. Then use the debit card payment system to provide instant access to funds. This process could save 1-2 business days, compared to the ACH clearing system.

Encourage applicants to do their part

Consumers are demanding faster approvals, but they’re willing to do their part. They search online for articles about the loan origination and underwriting process, because they want to proactively participate. 

Your marketing team should post informational articles on your website blog as part of a content marketing strategy. Include topics designed to help prospects complete their application more efficiently. Start by outlining the steps in the process, and tell your readers the data they’ll need to input during each step. Include a list of all the supporting documents they’ll need to attach to the completed application form. 

One of the benefits of content marketing in the digital age is that the information seeker can move from the blog article to the loan application with one click. The article content has already established your credibility, which increases the likelihood that they’ll complete your loan application instead of checking out the competition.

Automate manual identity verification processes

According to a recent article in Finovate, at least half of the origination budget gets eaten up by manual processing for identity verification and anti-money laundering programs.

An automated application review system can replace manual procedures, increasing operational efficiencies and reducing costs. TurnKey Lender comes pre-programmed with regulatory compliance rules (like GDPR) specific to your local jurisdiction, and it’s compatible with add-on regulatory technology (RegTech) software packages that automate identity verification.

Leverage lending technology platforms

Today it’s easier than ever for digital lenders, alternative funders, local banks, and credit unions to replace manual processes and outdated technology with a comprehensive, fully managed LaaS platform. These turnkey solutions use sophisticated automation software to review applications faster, and to make more accurate credit decisions. Your lending operation will originate more loans, reduce operating expenses, and increase yield by booking more profitable loans at an individual account level.

A superior LaaS program will include these service features: 

• automated origination and account servicing processes
• credit review via traditional bureau data, alternative bureau data, and proprietary scoring models 
• leading edge cybersecurity 
• regulatory compliant processes 
• digital money transfers for account funding and monthly payments
• omni-channel customer communications options
• consolidated cross-platform reports. 

The operating platform will include advanced functionality: 

• cloud-based system (easy to deploy, easy for your team to master)
• rules-based processes customizable for individual lender requirements
• outstanding technical and customer service support.

Next Steps

In a consumer marketplace where borrowers choose speed and convenience over APR, your lending operation must be positioned to deliver instant approvals, and 1-day funds transfer. 

One of the best ways to deploy an advanced automation and credit scoring system is to leverage LaaS technology. These fully managed, cloud-based lending platforms use state-of-the-art technology that’s continually upgraded. The credit decision software integrates traditional credit data, alternative credit data, and machine learning that supports your ability to approve more credit-worthy applicants, even those with thin credit files.

This is where artificial intelligence comes in as an adjunct, and a catalyst, to alternative scoring. To explain how this works, let’s step back for a moment and look at traditional scoring methods and their underpinnings.

Character versus credit score

In fact, there are at least two “traditions” in play here. 

Before the 1990s, assessing a retail loan involved a bank-based loan officer determining the applicant’s creditworthiness based on inputs such as income, indebtedness, prior-credit outcomes, bank balances, work history — and, to no small extent, “character.” Here character would be determined by a range of assessments from the quasi concrete (like the applicant’s prior dealings with the lender) to the relatively ephemeral (like the “standing” in the community). 

In other words, old-school inputs might suggest an applicant is good for the loan she sought in terms of her financial and employment standing, but the loan officer may not like something else about her. Maybe the trigger for rejecting her application, or imposing a higher interest rate,  was a particular character fault — the applicant’s notoriety as a boozer, for example — or maybe it was something frankly discriminatory such as her race, name, neighborhood, accent, or appearance.

By the late 1980s, however, lenders had hit on the concept of credit scores as a transparent alternative to making subjective value judgments about credit applicants, both in the name of fair lending and operational efficiency. To meet this nascent demand, FICO began compiling information on the financial behavior of individual consumers as determined by credit bureaus such as Experian, EquiFax, and TransUnion, and selling these scores to lenders. In time, the big three credit bureaus rolled out rival gauges to the FICO score, but the original remains the industry standard.

A new world of credit scoring

But FICO and other scores based on credit-bureau data form an incomplete picture of a loan applicant.  Borrower traits such as line-item spending habits, social-media comportment, and behavioral “tells” regarding financial obligations shed light in new corners. 

Along these lines, “psychometrics,” a branch of psychology for evaluating a person’s state of mind, is a fascinating new contributor to credit scoring. Applied to lending — and as an illustration of how granular alternative scoring can get — psychometrics show up in borrower traits such as:

• Self-esteem
• Confidence
• Honesty
• Familial relations
• Punctuality
• Responsibility
• Trustworthiness
• Money management and financial planning skills
• Risk aversion
• Organizational skills
• Consistency
• Mental agility

Coupled with additional insights into the applicant’s education level and personal savings and investments, these new data give lenders a sharper sense of the applicant’s ability to repay, and — writ large — a base on which to construct more durable loan portfolios.

The difference between a deluge and a credit score

Making sense of new inputs for credit scoring isn’t easy, however. Aside from raw computing power, it requires sophisticated artificial intelligence (in turn animated by dynamic machine learning) to rationalize an array of data from different sources to form a cohesive and legible internal credit-scoring system.

“With an applicant’s permission, our technology leverages information taken from bank accounts, retirement holdings and other transaction-rich sources to divulge spending habits, and monitor employment and non-employment income for a clearer picture of the would-be borrower,” says Elena Ionenko, a co-founder of lending-technology maker TurnKey Lender. “And because our perspective is global, we know that arming lenders with rationalized alternative scoring inputs equips them to thrive, even in markets where traditional scoring is either rudimentary or nonexistent.”

For instance, adds Ionenko, “Alternative scoring uncover hidden risks such as an applicant’s gambling expenditures or overdraft durations, and use them to make informed credit decisions.”

TurnKey Lender’s credit-scoring functionality, which combines traditional and alternative approaches, allows for dynamic customization. As a result, it answers the credit-assessment needs of mainstream lenders — such as banks, credit unions and finance companies — as well as new entrants. This growing list includes retailers of all sizes and types, medical practices, governmental agencies, NGOs, and invoice financiers.

Alternative credit scoring as a contributor to recovery

Now, with Covid and war pressing on economies, the importance of credit as a tool for rebuilding has come into sharp relief — along with the need for alternative-scoring approaches for lenders.

In the US right now, lending activity is centered on businesses, largely with a view to maintaining worker headcount by means of the Small Business Administration’s “Paycheck Protection Program” and other initiatives run by or for the federal government. Uniquely, government-backed PPP  lending is triggered not by creditworthiness, but a combination of need and the borrower’s ability to find a traditional lender willing to administer the low-interest loan.

Glaringly, however, there are no measures in place to speed the wheels of consumer lending — even though a full economic recovery is unlikely without such a kickstart. After all, the pandemic has eroded the credit standing of many who have lost jobs or seen wages cut. Getting money to borrowers in need these days will call for new ways of evaluating the creditworthiness of consumers.

“In these times, there is no way to exaggerate the importance of secure, reliable and intelligent scoring for lenders of all kinds,” says TurnKey Lender’s Ionenko. “The importance and utility of traditional credit-scoring is unassailable, but there is an acute need now for alternative approaches that lead to better decisions and, in turn, support more robust portfolios.” 

How TurnKey Lender factors in

Since the founding moment, TurnKey Lender team understood that in order to transform the lending industry in a meaningful way, we need to apply AI and Big Data to credit processing and risk assessment. That is why the core team included a department of PhDs and Artificial Intelligence experts that have done successful scoring projects for Fortune 500 companies. And while many traditional lenders were hesitant about making the jump to the new type of intelligent credit decisioning before the 2020 crisis, now the need to adapt to the borrowers’ demands is obvious and even unavoidable if they want to stay in business.

To achieve an unmatched level of credit decisioning accuracy, we use deep neural networks with self-learning scoring models based on both traditional and alternative evaluation approaches and data sources. Working with the clients’ data, the system learns to use prediction, classification, clustering, and association to process loan applications.  

For safety purposes, the system doesn’t just use the data the client is providing but also pulls the available information from the sources it’s synchronized with (like the credit bureaus, bank statement providers, borrower’s smartphone, or social media). All the data is processed by TurnKey Lender’s intelligent Decision Engine and is then presented in the form of a risk evaluation.  

All in all, risk assessment, borrower evaluation, and credit decisioning take our solution as little as 30 seconds. To draw a comparison, most traditional lenders can take up to 9 business days to complete this process.  

Let TurnKey Lender’s intelligent software analyze millions of data points in a matter of seconds while you grow your business.

But AI and Big Data, no matter how advanced and sophisticated, don’t bring much to the table unless they process the right borrower data. TurnKey Lender team analyzed the operations and credit decisioning flows of our clients in 50+ countries and dozens of business verticals to come up with optimal scoring models and decision rules that help weed out unreliable borrowers and pre-approve the loans that are most likely to be paid off on time. Not to mention, the dynamic selection and assignment of fitting credit terms to each application.

With all the heavy lifting done by the system, the employees’ time and energy are freed up to focus on business development and customer relations which translates into better performance.

TurnKey Lender Decision Engine comes with a proprietary AI-driven scoring model and decision rules built-in. Both are fully configurable, allowing you to set the factors you want to evaluate and assign values to them, based on what is more important for your operation.  For cases when there’s no sufficient data available to evaluate borrowers, you can use a dedicated TurnKey Lender Psychometrics app that evaluates the borrower’s smartphone data and behavior patterns.

At the same time, to make sure we leave no stone unturned, TurnKey Lender provides lenders with an option to also collect and process traditional data like credit bureau reports, bank account statements, and payment data.

Data from traditional and alternative sources goes through the TurnKey Lender Decision Engine which in turn analyzes it with machine learning algorithms and deep neural networks. Pre-configured integrations with 75+ relevant data sources and technology providers allow lenders to put their entire decisioning workflow on autopilot and focus on big picture things.

Interested in learning more? Schedule a call with one of our experts today!

But credit-rating agency Standard and Poor’s warns that conventional risk-assessment metrics may not be adequate for particular market segments. The reason? Measurements of suitability for consumer and B2B lending have lost much of their predictive power for businesses and individuals impacted by pandemic-related work stoppages. This makes it hard to distinguish temporary setbacks from the advent of new normals for would-be borrowers in some industrial sectors.

The answer to this seemingly intractable problem? Automated credit-risk management, which can process more inputs and render more decisions faster and more accurately than ever before.

“Credit risk refers to the chance of loss from a borrower’s failure to repay debt,” says Dmitry Voronenko, co-founder and CEO of TurnKey Lender, a top lending software provider. “In turn, credit-risk management refers to measures taken to mitigate losses by understanding the adequacy of a bank’s capital and loan loss reserves at any given time as well as measures taken in credit ‘decisioning’ and loan origination to make applicants’ financial situations more transparent to lenders.”

Learn about TurnKey Lender’s Decision Engine in detail to outperform any and all competitors and reduce credit risk.

Credit-risk controls impacted by internal and external pressures on lenders

Automated, or digitally enabled, credit-risk management is a form of gatekeeping that incorporates big-data inputs and artificial intelligence to help lenders make better decisions faster than ever before. Additionally, it helps lenders:

• Tap into unbanked and underbanked consumers — 7.7% and 17.9% of US adults respectively, according to the Federal Reserve
• Achieve automatic compliance around anti-money-laundering and know-your-customer regulations

And while the coronavirus pandemic quickened the pace of digital adoption for the sake of social distancing, the shift to automated credit-risk management was underway before Covid-19 quickened the pace of digital adoption for the sake of social distancing.

“External and internal pressures are requiring banks to reevaluate the cost and sustainability of their risk-management models and processes,” McKinsey says in a 2016 report called “The value in digitally transforming credit risk management.” The pressure came from regulators, emerging fintech competitors, company-stock holders, and the banks’ own customers, the report adds.

Due to more stringent capital requirements in the wake of the 2008 Financial Crisis, higher fines for noncompliance, and lagging cost efficiency, the share of risk and compliance in “total banking costs” went from about 10% in 2012 to a projected 15% for 2017, McKinsey reported in 2016. “This puts sustained pressure on risk management, as banks are finding it increasingly difficult to mitigate risk through incremental improvements in risk-management processes,” according to the consultancy.

Another trend changing the face of lending centers on evolving external and internal expectations.

Externally, customers want mobile and digital solutions. The global digital lending market, valued at $4.87 billion in 2020, is expected to expand at a compound annual growth rate of 24.0% from 2021 to 2028, says Grand View Research. Already by 2018,73% of consumers were using online banking channels at least once a month, according to Deloitte.

High indebtedness plus systemic disruption calls for better risk controls

Meanwhile, executives and business strategists within lending organizations have come to expect timely and accurate credit-portfolio reports to help them spot emerging troubles, improve efficiencies, calibrate marketing initiatives, and inform pricing.

McKinsey’s prescription is short and sweet. “Banks need to digitalize their credit processes,” the company says. “Lending continues to be a key source of revenue across the retail, small and medium-size enterprise, and corporate segments.”

While most of McKinsey’s assessment still holds water, digitalized credit-risk management has emerged as a must-have not just for banks, but also for a growing array of non-bank organizations — from retailers to capital-gear providers, medical practices, and nonprofits — that seek to bypass the middleman and provide white-label, software-based lending and loan management directly to customers. And this trend was well in evidence when the pandemic took hold.

Pre-Covid, lenders had become alarmed about rising debt levels in the US — $14.1 trillion in February 2020, spurred mainly by mortgages and credit cards. Now, with households under pressure from lost wages and other pandemic-related financial hardships, lenders are monitoring and identifying would-be borrowers who are under financial strain in their due diligence.

Fortunately, credit-risk-management tools are more robust than ever

“At the same time, however, lenders equipped with the right lending technology can now look to alternative credit scoring to get more holistic views of applicants,” according to lending-tech expert Voronenko. “In fact, lenders can use alternative scoring to safely extend credit where — using only traditional means derived from applications and credit-bureau reports — such probing wasn’t feasible before.”

With artificial intelligence shedding light on otherwise impenetrable data sets to include factors established as behavioral finance “tells,” lenders can compare permission-based inputs around spending and bill-pay habits to find low-risk applicants among some that would be considered high-risk using only traditional scoring methods.

Companies eager to leverage advances in integrated lending software to establish their own credit facilities may be familiar with traditional and other third-party lenders, but a close comparison of old-school processes with the existing state of the art can be impactful.

Decision time

• Traditional underwriters can take up to nine days to collect and analyze all relevant data and make a final crediting decision.
• With advanced, cloud-based lending software, decisions can be made — at the barest minimum — twice as fast. Often decisions can be rendered in a matter of minutes or in many cases instantly once scoring and decisioning are finetuned.

Scalability

• Using manual and other traditional procedures, it can be hard to scale due to operational expenses that grow as new customers are onboarded.
• With advanced and fully-supported lending software, risk scores and loan decisions are automatic — virtually “hands-free” from a staffing perspective.

Human error

• Everybody makes mistakes. But in the realm of risk management, mistakes can impede revenue flow and hurt your brand.
• Automation — especially when it’s as comprehensive as the leading lending-tech vendors — makes human error much less common and much less costly. Underwriters can review applicant information in a workspace designed to set such errors in sharp relief and suggest quick and easy remedies.

In-person requirements

• In old-school configurations, would-be borrowers have to show up in person, wait in line, answer questions, and generally waste time.
• Scorecards linked to automated risk-management systems quickly reject low-quality applications, saving both sides a great deal of time and trouble.

“The pandemic hasn’t triggered the need for automated credit-risk controls, not in isolation,” says TurnKey Lender’s Voronenko. “But it is showing — and with more clarity by the day — that new risk-assessment tools, drawing on functionalities as diverse as machine learning, dynamic workflow analysis, and advanced software integration, enable lenders to make better, and sometimes more inclusive, decisions.”

Functionally, automated underwriting is an algorithm-driven process for evaluating the risk of specific transactions. Effectively, it takes humans out of the loop. The result? Decisions, based on the lender’s customizable guidelines, that are more accurate, and rendered much faster than the “analog” alternative. 

Why underwriting automation isn’t just for banks  

Automated underwriting has spread from credit cards and mortgages in the 1990s to banks and other traditional lenders as the internet took off. “Now a host of retailers, equipment suppliers, and medical practices” encompassing not just traditional lending but also “other credit scenarios such as invoice factoring, leasing, and even pawn-shop transactions employ automated underwriting,” says Elena Ionenko, co-founder and operations chief for TurnKey Lender, a pioneer in the space. “It extends to any financial transaction aided by an assessment of a would-be borrower’s ability to repay the loan amount plus interest — and that’s true whether it’s a consumer buying a lawnmower or a civil engineering firm looking to deploy a half-dozen commercial drones for surveying inaccessible terrain.” 

So what, past the availability of the technology, is behind this new surge in uptake? In a word, economics. A lender’s ability to gauge a loan applicant’s creditworthiness will increase the quality and scale of its loan portfolio and increase customer satisfaction. In an increasingly digital world, lenders mired in paper-based vetting procedures operate at a competitive disadvantage that is virtually impossible to overcome.  

Book a demo to see TurnKey Lender’s AI-driven underwriting software in action.

Benefits of Automating Commercial Underwriting   

To show how this economic advantage plays out, here are 10 of the most important ways automated underwriting makes lending more attractive, efficient, and compliant. 

1. Enhanced decision making: Automated underwriting isn’t just about faster decisions. It’s also more accurate than its spreadsheet-based forebears, which is, in turn, fairer to everyone involved. After all, while people are notorious for having bad days and succumbing to stress from time to time, algorithms never burn out or slip up. Crucially, automated underwriting can also take account of factors relevant to approval — such as spending habits and social-media comportment — that go beyond old-fashioned assessments based on credit scores and biographical questionnaires. 
2. Consistency: The rules the lender sets for automated credit decisioning can be both customized to specific clients or client types and consistent with bedrock policies. Say goodbye to guesswork and interpretation. 
3. Portfolio yield: Automated underwriting lays the groundwork for predictive models that pinpoint optimal loan rates and terms while helping lenders of all kinds improve portfolio yield by selecting the most profitable customers. 
4. Productivity and workflows: Once again, lenders and applicants alike benefit from a credit-underwriting system that saves time for everybody involved. Faster approvals and streamlined processes for the lending lifecycle from applications to final repayment make for more accurate outcomes and more cost-effective lending. 
5. Analytics: Robust and accurate digitalized underwriting makes for actionable lending intelligence derived from accessible data analytics that can guide operational improvements and create new marketing and UX insights. 
6. Compliance and fraud detection: Underwriting automation strengthens fraud-detection efforts by means of predictive analytics. In addition to raising red flags, a robust automated lending platform will allow updated rules to take force across the entire system, instantly. In this way, the lender is much likelier to remain compliant and on guard against fraud. 
7. Scalability: Automated underwriting is scalable in that an influx of new customers doesn’t necessarily call for more people to perform credit-check responsibilities around classification, analysis, stacking, and extraction. And in our context, automated underwriting is understood to be part of a loan processing system that encompasses other critical functionality around loan and loan-portfolio management for end-to-end scalability that allows companies to cover more ground without having to add headcount. 
8. Affordability: This is especially true of automated underwriting linked to systems with a modular format. “With TurnKey Lender, you can start small and add functionality as needed,” says Ionenko. “As a reflection of our clients’ preferences, it’s as far from all-or-nothing as it gets.” 
9. Oversight: Automated underwriting — like all elements of lending automation — leaves tracks. This analytical prowess, already mentioned, also helps managers and executives stay on top of things generally, and enables troubleshooting and internal audits. 
10. Customer experience and marketing: As more companies in more fields employ automated underwriting, more consumers and companies eager to secure financing have come to see it as a must-have in a financing partner. They may not know that credit decisions hinge now on sophisticated algorithms and artificial intelligence with the ability to learn new lessons and make sound inferences. They may only know that their credit applications are processed faster than ever before. But that’s enough to feed into positive word-of-mouth publicity and enhance the lender’s overall marketing efforts. 

Ten is a nice round number, but it wouldn’t be hard to extend the list of the benefits companies can derive from automated underwriting. To this end, terms like “Convenience,” “Ubiquity,” and “New revenue from fees” spring to mind. But you’d be hard-pressed to make the point better than TurnKey Lender’s Ionenko. 

“From the lender’s point of view, underwriting is the first gate-keeping function in every loan application,” says the tech executive. “Making sure it goes as quickly, accurately, and pleasantly as possible can set the tone for the entire engagement, lead to repeat business and, overall, enhance the average value of your customers.”

The traditional approach to loan-portfolio management puts collections and overall performance on one side, and origination on the other, with decisions that should be closely coordinated made by separate departments, often deployed across distinct software systems.

But that’s changing as senior managers work to strengthen ties between departments and advances in artificial intelligence allow for more nuanced — and more inclusive — procedures for vetting would-be borrowers.

Putting origination on an equal footing with other parts of loan management does more than provide holistic overviews. It puts extra resources into gatekeeping, providing a crucial first step in credit-risk evaluation and fraud detection, a must-have for overall portfolio health.

It also equips lenders to compete in today’s tough economic environment, a byproduct of business shutdowns, workplace furloughs and the general public’s hesitancy to congregate in a pandemic. In this context, it’s vital that alternative data inputs be uniformly formatted, easy to interpret, and available as an aid to decision making.

Converting such data into a scoring model requires advanced artificial-intelligence tools and processes, tons of historical data, and years of hands-on experience. But AI-derived credit scoring is vastly more accurate than traditional approaches — which hinge on credit-bureau scores and application responses — and provides lenders with the confidence they’re getting a fuller picture of applicants and approving better-performing loan, even in hard times.

The result? More good loans and better portfolio performance.

Meanwhile, there are real-world consequences to sticking with outmoded scoring models. Because old-line lenders aren’t usually able to change underwriting standards without accessing flexible, easy-to-configure and easy-to-deploy technology, it’s likely banks will experience a new wave of non-performing loans in the next year, according to industry consensus.

Read full article of Forbes or schedule a live TurnKey Lender demo today.

Alas, late in October, the Canadian central bank issued a Monetary Policy Report suggesting that hard times will continue, perhaps all the way through 2022.  

“The Bank estimates that over 2020–23, persistent scarring effects of the pandemic on the labour force,” the Bank of Canada writes. The word “scarring” is a favorite of chief central banker Tiff Macklem, who has been issuing periodic warnings of tissue damage to the Canadian economy since the pandemic was declared in mid-March 2020. 

Assessments like this should prompt non-traditional lenders — conceivably any business that might want to extend credit, from retailers to car dealers — to rethink how they gauge loan applicants’ creditworthiness. Why? Because businesses of all sizes in every province and territory will be looking for capital to help them through a recession made worse by: 

• A pandemic that isn’t over, resulting in 
• The need for ongoing social-distancing measures that can snag business recovery, such as expensive new workplace configurations and equipment  

• Pinched household budgets and other recession-related woes, leading to 
• Subdued consumer spending  

Meanwhile, many lenders will be making credit decisions based on inputs that aren’t adequate to the times.  

Credit scoring in Canada in the face of uncertainty 

Applying old-school analysis where new market conditions prevail could curtail lending and stall economic activity. For lenders of this ilk, being behind the times could jeopardize opportunities to make sound and profitable loans. 

Download the free white paper now:

HOW-TO-WIN-KEEP-CUSTOMERS-WITH-RETAIL-FINANCING

Lenders traditionally rely on credit scoring based on objective financial data and subjective views on some of the would-be borrower’s non-financial traits. In this approach, the financial data includes the prospective borrower’s credit history, and line-item comparisons of historical financial statements submitted by the applicant. Among traditional non-financial inputs are the would-be borrower’s profile (employment, status, degrees, home and car ownership), a qualitative assessment of the borrower’s previous dealings with the lender, and, for business loans, relevant business plans. 

In this approach, financial data has more weight in determining how stable and efficient the applicant is when it comes to their finances. 

Although recent word from drug maker Pfizer seems to bode well for a Covid-19 vaccine, the company’s claims have not been verified, and, given the logistic challenges, widespread distribution of a vaccine is unlikely before, at the earliest, mid 2021. For now, it’s prudent to remember we don’t actually know how long either the pandemic or its economic aftershocks will last. 

“These uncertainties erode the rationale behind applying only traditional credit analysis,” says Elena Ionenko, co-founder and business-development head of lending-technology provider TurnKey Lender. “After all, real-time financial data can be as indicative of repayment as historical information.” 

Adds Ionenko: “This analysis can be performed on a continuous basis — triggering monthly or quarterly reports — that provide dynamic updates on the loan, which helps lenders see how the borrower is coping in real time with the challenges of a recession, while comparing these results to pre-crisis data.”  

Guided discretion 

In a typical lending scenario, lenders start off by “scoring” loan applicants to determine the likelihood of their returning an amount owed with interest in a given period. Most use third parties such as Fair Isaac, whose Canadian FICO scores assign numerical values between 300 and 900, with 900 indicating maximum creditworthiness. 

For the most part, this traditional scoring relies on factors such as: 

• How long the applicant has been using credit 
• The amount and type of debt an applicant already has 
• Current interest rates on outstanding accounts 

Lenders use these reports to generate a risk profile of the applicant, which helps lenders determine whether to make a loan in the first place, and the terms of any loan that’s approved. Obviously, applicants with low credit scores tend to be assigned higher rates of interest than those with higher scores, though the ultimate decision is made by the lender, with FICO inputs used as guardrails. 

Of course, the pandemic has eroded the credit standing of many who have lost jobs or seen wages cut, necessitating new ways to evaluate consumer creditworthiness. 

Low FICOs and the unbanked 

For example, a FICO score won’t tell if an applicant has lost her job or seen her income dip in the public-health crisis. One solution to this increasingly widespread problem is working with alternative data sources for determining creditworthiness. 

One of the most reliable sources of information? An applicant’s bank accounts.  

Read about TurnKey Lender Bank Statement Scoring on our knowledgebase.

Some lending-technology providers empower lenders to examine applicants’ bank accounts and track transactions to take note of spending habits and monitor employment and non-employment income including such responses to the pandemic as stimulus payments, forgivable loans, and unemployment-insurance proceeds.  

Some advanced lending-tech firms equip lenders to see these data points, and more. For example, alternative scoring can uncover normally hidden risks such as an applicant’s gambling expenditures and overdraft durations and apply them to credit decision making. 

Read about TurnKey Lender’s AI-Powered Decision Engine.

And for consumers who are unbanked or underbanked — 18% of Canadians, according to ACORN Canada — alternative scoring is a must. More so when you take account of LexisNexis research indicating that 51% of traditionally unscorable applicants in the US are as creditworthy as consumers with high traditional credit scores. 

Augmentation, not replacement 

“This doesn’t devalue traditional credit scoring,” says TurnKey Lender’s Ionenko. “For predictive power, no one alternative approach is as formidable as credit-bureau input.” Alternative data points are more numerous, more scattered, and less organized than the data that contributes to a traditional credit score, she explains. “This means neural networks and other AI-based tools are required, which is an approach we pioneered.” 

Fortunately, these resources are now available to lenders, and the additional intelligence this normalized alternative data provides helps lenders understand their customers better, make better loans, and build better-performing loan portfolios. 

Years using alternative data sources to assess consumer creditworthiness in the developing world has paid off for some lending-tech makers, giving them a solid sense of a loan applicant’s relative riskiness. As a consequence, lenders plugged into such technologies in developed economies have new ways to gauge the “lendability” of consumers — and new ways to contribute to the economic recovery of markets in Canada and around the world. 

In particular, fintechs know that bank-statement data, enhanced by deep machine-learning and time-tested artificial intelligence, means better outcomes for borrowers and lenders alike.

TurnKey Lender ULM has a Canada edition with unique customer details collected through the application form and preconfigured integrations with payment providers, credit bureaus, and more which allows for unmatched decisioning accuracy and processing speed. 

To learn more reach out today for a personalized demo tailored to your business.

“Open banking” is a financial-information exchange methodology and a source of innovation that’s transforming banking as we know it. It gives third-party financial-service developers and providers permission-based access to customers’ financial data from financial institutions via “application programming interfaces,” or APIs.

In plainer terms, open banking has the potential to change everything about banking, from who gets to be a customer to who provides underlying services (and on what terms).

Philosophically tied to the information-age principle of “open innovation,” open banking includes the “open data” concept, which holds that some data should be freely available for everyone to use as they wish, without restrictions from copyright, patents or other claims to exclusivity, according to one study. At root, open banking is based on the idea that data can be used to improve customer outcomes along the lines of faster, fairer, and more accurate service delivery — and that without it, the potential for innovation is blunted by considerations of data ownership.

“Many financial institutions welcome APIs begrudgingly, as a cost of doing business,“ says Elena Ionenko, co-founder and business-development head of lending-software maker TurnKey Lender. “But we see them as powerful tools for making the most of relationships between customers and banks — or bank-like entities, keeping in mind that it’s not just banks playing a role these days.”

The case for open banking

In fact, lending by retailers, medical-service practices, capital-equipment providers is on the rise, and their success is as dependent on unhindered open-banking inputs as any old-line bank. Just in terms of mortgages, non-banks originated 53% of US loans in 2016, but 64% of home loans for black and Hispanic borrowers, according to a Brookings Institute report cited by the Washington Post.

With open-banking coming to the forefront as an equalizer in the marketplace, fintech innovators have APIs in mind at virtually every turn.

“Part of the agility you need to compete as a lending-tech provider these days is reflected in how configurable you are,” says Ionenko. “Nothing is hardcoded here because we know we have to be ready to support any lender anywhere in the world at any time. We win business because we’re API-minded, and have been from our inception.”

Meanwhile, informed consumers see putting their (usually anonymized) financial information out there for use by third parties via APIs as a fair exchange for better service at better prices and, in some cases, for more accurate credit scores.

Imagine paying for your daughter’s guitar teacher for this week’s Zoom-based lesson with your smartphone. That’s something you can do right now, and it’s all thanks to open banking, which allows you to buy items or services with a phone app that’s linked to your bank card or PayPal account. The fact you can pay that way involves a veritable ecosystem of permission-based data sharing, but the technology and necessary protocols are more than up to the challenge.

Integration and interpretation of data

In practical terms, open banking is dependent on database integration, and the mining and management of data that provide relevant customer insights. The timeliness of a loan applicant’s rent and car payments can shed light on the ability and willingness to meet additional financial obligations. This can help determine loan scores that are more nuanced than old-line credit scores — which have in turn have been criticized for perpetuating race, gender, and age disparities in lending decisions.

In fact, innovations in loan-servicing software show how open banking can work to everyone’s advantage. Typically, lenders participate in open banking in one of three capacities.

• They seek to originate loans as a lender
• They want to create a proprietary service that complements their loan products, like an enhanced credit score
• They are partnered with such complementary service providers

Whether the offering is a direct loan or another service that can help establish or improve relationships between lenders and customers, it can function as a lead generator that owes its power and durability to an open-banking environment.

Open banking as a permanent disruptor

The fact that customers have to “opt-in” to benefit from open-banking programs puts a constraint on lenders and related service providers because customers have to see a clear-cut benefit from participation. Incentives of this type in the lending realm include:

• Easier access to loans through point-of-sale loan-origination systems
• More nuanced loan decisioning derived from more credit-score inputs with better consumer outcomes
• “Favored client” status with access to offers and incentives that can help establish enduring customer-lender relationships

In some jurisdictions, oversight of open-banking systems has been formalized. In this category are the European Union, the UK, Australia, and Hong Kong. Meanwhile, in most of the Americas, and in the Asian-Pacific region, open-banking protocols are on — or quite close to — near-term legislative agendas, according to news reports. And it’s safe to assume that compliance criteria will change as open-banking technologies evolve.

“The future of open banking and API development is exciting to contemplate,” says TurnKey lender’s Ionenko. “The concept and the interfaces that make it real add up to a disruptor for which pre-digital legacy systems are a clear hindrance. The future of banking belongs to providers who are technologically nimble enough to align themselves organically to the brand at the front of every customer engagement.”

For some businesses, COVID-conscious office layouts, plexiglass partitions, and staggered shifts have come into view as components of workplace social distancing. For others, remote and contactless commerce will be make-or-break, with e-lending playing a prominent role in helping businesses and consumers rebuild around an amorphous new normal. 

Rising demand for loan origination systems 

E-commerce has been around since the mid-1990s at least. But fully supported e-lending took wing later, during and right after the Financial Crisis of 2008. Its first iterations formed the underpinnings of web-based lending through traditional players such as banks and credit unions, along with some direct-to-consumer forays by fintechs.  

Since the mid-2010s, however, online lending has emerged as a force in retail, equipping businesses to extend credit to consumers at the cash register or on the road, with decisioning analytics performed in minutes rather than hours or days — and in some cases without the need for financial intermediaries.  

“The point is to give businesses more opportunities to close more sales and deepen customer relationships while creating stronger loan portfolios and providing mission-critical business intelligence,” says Dmitry Voronenko, CEO and co-founder of lending-platform provider TurnKey Lender. “We’re seeing rising demand from traditional lenders and retailers, as well as from businesses involved in capital-equipment factoring, trade facilitation, and lease lending.” 

Adds Voronenko: “Boiled down, it’s at the point now where a business that wants to extend credit can do it intelligently, efficiently, and on its own terms. All you need is internet connectivity.” 

That’s all great, of course, but how does a business shop for a loan-origination system, or LOS, in the midst of a pandemic?   

Best practices for LOS shopping 

For Voronenko, like most experts in the field, it comes down to matching needs with options in the marketplace, a process that starts with establishing a basic understanding of the lending space. To help lenders, whether novice or established, succeed in their search for a flexible and reliable LOS, we’ve compiled the following checklist. 

1. Get a good overview. TurnKey Lender recommends that lenders, especially new ones, start getting up to speed on their understanding by visiting online resources, including its own website. Operating on the premise “an informed customer is the best customer,” an LOS’ salesforce is another great resource for prospects looking to match their needs to particular system providers. In addition, user-rating platforms like Capterra and G2 can be great sources of information about particular offerings’ real-world performance. 
2. Every lender is different. Find an LOS provider that understands that. Effective responsiveness requires speed as well as precision. Lenders want digital capabilities they can roll out quickly to support customers in the current crisis and beyond. An LOS provider like TurnKey Lender provides solutions that are configurable for each client firm using flexible flow-building and rules-management tools that makes its time-to-market hyper-competitive. 
3. Increased importance of data security and customer privacy. TurnKey Lender demonstrates its readiness on this front through third-party certifications. The main gauge for best practices in safeguarding the lender’s data and their customers’ peace-of-mind is the Open Web Application Security Project, or OWASP, standard. TurnKey Lender is compliant with this standard, complying with the widely recognized ISO 27001 standard of information security, and the ISO 9001 standard for its quality management. These certifications ensure TurnKey Lender meets or exceeds all statutory and regulatory requirements. 
4. Lenders want a one-stop solution. Banks and other tenured lenders are turning away from siloed solutions for different stages of loan origination and processing, and for different credit products. TurnKey lender is at the forefront of this convergence, with the flexibility and power to support loans of every sort — all on one platform that features consolidated reporting for immediate insight on credit portfolios across product types. 
5. An LOS should equip the lender to compete, even with the big guys. TurnKey Lender is the “Intel Inside” many large- and middle-market lending platforms, through which it has processed millions of loans. These institutions achieve fast and accurate application processing, and superior customer experiences. An LOS provider that knows how big lenders operate can make its clients competitive with established digital lenders on all fronts. 
6. The right LOS will simplify operations, not complicate them. Does your business have the staff and the institutional knowledge to develop, maintain, and manage an advanced lending software platform on site? If not, you may take a cue from many small- to midsize lenders and opt for a cloud-based “lending as a service” model — which is also the choice these days for many large organizations eager to balance cost-savings with data security.  
7. In the current crisis, speed to market is paramount. Because its uses flexible workflow and rules-management tools, an LOS provider like TurnKey Lender can get your lending operation up and running quickly, so you can get to work extending credit where it’s needed most. 

As an additional incentive to businesses contemplating the move to e-lending, it’s worth noting that consumers’ online habits seem to have changed in the pandemic, with some assuming the transition will stick.   

Lenders, welcome to your digital future 

According to an early May 2020 survey of consumers by payments-industry tracker PYMNTS, 26% of generation X who shifted routines to online platforms don’t plan to move back offline once the pandemic is over or under control. Perhaps more impressive, 21.7% of boomers and seniors say the same, as do 23.8% of millennials, and 24.6% of “bridge millennials” (between age 32 and 40). 

“The stage was set for an accelerating migration to e-lending before the coronavirus pandemic — as much a function of raw demand as where smart technology is taking us as a society,” says TurnKey Lender’s Voronenko. “But this unfolding event, as tragic and disruptive as it is, has significantly heightened both need and awareness of it. In short, it’s a solution whose time has come.” 

Reach out to the TurnKey Lender team today to learn more.

It’s not clear whether Congress will trigger an additional round of stimulus to counter the economic effects of the novel coronavirus pandemic; stimulus that has included government-guaranteed lending to businesses. What is clear, according to definitive new findings, is that the US economy was already in recession a month before a contentious and patchwork approach to “social distancing” got going in March.

As a result, lenders must reconsider how they assess business-loan applicants’ ability to honor the terms of the loan.

Another thing that seems likely is that businesses of all sizes in every state, district, and territory will be looking for loans before and after the stimulus spigot is finally wrenched shut. Many of these enterprises will be seeking help to get them through a recession exacerbated by:

● A contagion that may not have run its course

● The need for ongoing social-distancing measures that can impede business recovery, such as less restaurant seating, and expensive new workplace configurations

● Truncated consumer spending and other recession-related woes

● The usual uncertainty that takes hold in presidential election years

In this environment, many lenders will be making credit decisions based on traditional inputs that aren’t adequate to these times. In a macro view, applying old-school analysis to new market conditions could curtail lending and stall economic activity. For lenders, it could mean losing out on opportunities to make well-performing loans.

Read the full article on Forbes: How To Approach Business Creditworthiness Assessment During A Crisis-Fueled Recession