Loan Origination and Loan Management Made Easier and More Lucrative with AI

Articles Digital Lending

Artificial intelligence, or AI, is changing the face of lending, empowering digital alternatives to outdated business processes while encouraging new entrants — from retailers and medical practices to trade-order financiers and equipment-leasing firms — to become effective lenders in their own right. 

AI takes account of data to inform decisions or predictions, broadly mimicking human cognition. It’s supported by “machine learning,” which employs algorithms and statistical models to perform many binary (“if this, then that”) tasks at once, drawing on patterns and inferences rather than requiring explicit case-by-case instructions. This helps lenders process loads of data from different sources by enabling them to identify, categorize, and make decisions based on multiple data points from multiple datasets — all in less time, typically, than it takes to wink. 

Before AI, the credit-worthiness of prospective borrowers was determined by scorecards “filled in” by consumer-credit agencies such as TransUnion and Experian.  All the checks, third-party data gathering and analysis can take traditional risk assessment approaches weeks. If we take TurnKey Lender’s Decision Management System for comparison, it does the same in under 30 seconds.  

“This approach has several advantages, including accuracy and ease of oversight,” says Dmitry Voronenko, co-founder and CEO of TurnKey Lender, a lending-software maker. “On the downside, scorecard methodologies simply aren’t equipped to handle the very big-data inputs that can make a lending operation more efficient.” 

Artificial intelligence as a tamer of change agents  

This was an acceptable trade-off as long as lenders were content to sort through limited data sources — like loan applications, the lender’s internal databases, and credit-bureau scores — for information on applicants. But now, thanks to the widening sweep of digitization, there’s a deluge of alternative data sources on prospective borrowers, including social networks, mobile devices, payment systems, and web activity.  

The speed and accuracy of AI mean lenders can use it to manage one or more of the following business-change agents, from which no lender is wholly immune. 

  1. Customer expectations: Consumers, accustomed to online banking and e-commerce, expect easy, convenient, and personalized borrowing experiences characterized by fast decisions and fast fund delivery. Machine-learning-backed AI makes loan-underwriting decisions within minutes or seconds rather than the hours or days it takes to do it the old-fashioned way. 
  2. Operational challenges: AI (deep neural networks) improves the quality of insights you get from data to ensure the correct decision formulations are applied to the type of data in question. Without AI, lenders are forced to apply data-integration models that simply weren’t designed to handle the amounts and varieties of data inputs available these days.  
  3. Sub-optimal customer overviews: A wider variety of inputs about loan applicants doesn’t just aid in AI-fueled underwriting. It also builds a more complete picture of the customer than pre-AI accounting systems do, even with new inputs.  
  4. Regulatory requirements: Lenders fighting to stay competitive by monetizing the reams of customer data available to them are under scrutiny in many jurisdictions to ensure they’re actively safeguarding that data and harvesting it compliantly. Best-of-breed AI processes adhere to local rules and restrictions and allow for intuitive compliance workflows tailored specifically to your business model and local regulations. 

These solutions benefit businesses looking to distinguish themselves from rivals with the speed and accuracy of their loan origination. “False declines” — loans not granted for due to faulty data interpretation — impacts 15% of US consumers, and costs lenders nearly $120 billion a year, according to research firm Javelin Strategy.  With a modern AI-driven loan origination and management solution, this enormous opportunity can turn into a part of your portfolio and a lifeline for thousands of SMEs and individuals in need of financing.   

Built-In AI in TurnKey Lender

TurnKey Lender applies deep neural networks and machine learning algorithms for a variety of purposes on many stages of the loan’s lifecycle.

The biggest and the most important is the Decision Engine. The AI within the scoring models analyzes millions of data points based on both traditional and alternative evaluation approaches and data sources. Working with the client 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 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.

Some other unique artificial intelligence applications in TurnKey Lender include:

  • Business performance analytics
  • AI-Driven Bank Account Statement Scoring
  • Employee Performance Management
  • Psychometrics scoring
  • Borrower’s Geolocation Tracking and Analysis
  • Customer Rating

More data, and more capacity to make sense of it  

AI makes loan origination less prone to human error and more reliable. It does this by freeing lenders from having to rely on credit-scoring agencies and customer inputs to evaluate loan applications. Would-be borrowers can in fact grant lenders permission to access “alternative scoring” inputs around things like customer’s spending and budgeting habits, social-media usage, and family situations to the extent they shed light on the customer’s ability to repay a loan on schedule. This, and AI’s role in making sense of all this additional data, makes false declines less likely, and lays a foundation for better overall loan-portfolio performance. 

AI can also play a role in loan management by helping lenders spot behavioral patterns that could lead to default — and trigger outreach to the borrower that’s geared to avoiding this outcome, or lessening its impact for the lender. Reducing default risk in this way not only prevents loss, but it can also even preserve available credit for worthy borrowers. 

“It’s hard to overstate the importance of AI to improving loan origination and management, which in our case is proprietary,” says TurnKey Lender’s Voronenko, a data scientist with a doctorate in artificial intelligence. “This approach to scoring is making consumer lending and business crediting faster, more secure, and more rewarding for lenders and borrowers alike.” 

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