In the realm of digital lending, artificial intelligence isn’t just an add-on, or something that’s conceptually nice to have but not really required.
On the contrary, a high level of intelligent automation is an absolute must for digital finance, which empowers organizations of all sizes and kinds to make smart, risk-adjusted lending decisions without help from back-office personnel or input from slow-moving credit committees. AI is quite a vague term used for marketing far more commonly than for solving actual problems. Artificial intelligence right now simply refers to processing large amounts of data and gradually improving the way your software understands it. And in digital lending the amount of data to be processed is huge and the means to process it include machine learning, deep learning, natural language processing (NLP), and image recognition.
Setting the scene
Globally, the digital lending market accounted for roughly $10.7 billion in 2021, according to Research and Markets. By 2026, the consultancy expects the number to hit $20.5 billion, for a five year compound annual growth rate of 13.8%.
R&M attributes this growth to several factors:
- Rapid expansion in Asian-Pacific markets where digital banks and credit unions get the most traction
- The rise of collaborations and partnerships between technology innovators and name players in finance and payment processing
But the number-one reason for the rise of digital banking around the world, according to R&M?
- The rise and accelerating adoption of AI, machine learning, and related technologies in the five-year forecast period
The rise of the AI market is even more impressive — even in a shorter time frame. Technavio sees a CAGR of 29.4% for AI in the years between 2019 and 2023, with 61% of this growth occurring in the US and Canada. In terms of revenue growth, Statista sees the global AI market generating about $71 billion in 2023 and rising to $126 billion by 2026.
What’s really going on with AI in digital lending
“Lending is becoming democratized, and it’s happening rapidly,” says TurnKey Lender’s CEO and co-founder Dmitry Voronenko. “A lot of that is down to digitalization, which is making turnkey loan origination and management as universal and straightforward for businesses as payments processing.”
But digitalization — the process of converting information into digital formats — isn’t the whole story. “To make the most of the lending opportunities available to organizations through digitalization, AI must be present and focused on rigorously practical applications,” according to Voronenko, who has a PhD in AI. “With robust AI capabilities, you can dramatically improve the performance of your lending business in any setting.”
TurnKey Lender’s AI-based scoring technologies, currently used by businesses in more than 50 countries around the world, eradicate guesswork from credit decisioning, allowing for an even-handed and dispassionate assessment of all applicants, even those in “high risk” categories. The company’s decision-management system can take traditional or alternative data and learn, predict, categorize, and match applications for decisioning. To achieve this, the system harnesses the power of deep neural networks and self-learning scoring models to evaluate borrowers in real-time.
With other financing-software vendors in play, it can take days for a funding applicant to get a credit decision. It takes TurnKey Lender’s decision-management system about 30 seconds to draw a conclusion — one that takes account of an array of critical traditional and alternative inputs.
Bank statement scoring
But the value of TurnKey lender’s AI goes beyond mere speed. Using deep neural networks backed by sophisticated mathematical modeling and continuous machine learning, the platform’s Bank Account Statement Scoring Model was engineered to chew through big datasets with ease. This scoring model combs through the applicant’s banking history to characterize cash flow, spending patterns, and other account trends that shed light on complex non-linear interconnections between variables. And vitally, the Bank Account Statement Scoring Model accomplishes this while “teaching” itself new lessons that’s accretive to its overall and ongoing utility.
Underwriting and credit decisioning
TurnKey Lender uses comprehensive credit decision flows based on proprietary AI-powered decisioning algorithms. TurnKey Lender applies machine learning and deep neural networks to automate credit scoring based on traditional and alternative risk assessment data. This allows lenders to streamline and fully automate their credit processing and achieve almost instant loan decisions. Underwriting of TurnKey Lender platform includes functionality for in-depth risk scoring, borrower evaluation, decision rules checks, loan agreement generation, loan offer management, and more.
Collections are the 2nd most challenging part of lending after underwriting. But now that lenders have access to a deep well of data, any suspicious or troubling actions or issues with the borrower can be communicated to the lender before they even take place through collection scoring. Whether it involves identifying delinquency early or determining when and how to send messages to have positive effects, debt collection is backed by a complex system of inputs, algorithms, and analysis.
Knowing your customer is a mantra in compliance, meant to combat criminal finance. But it’s also a stable base for pinpoint marketing. With permission, the system determines a rating based on a borrower’s interaction with the platform and staff, as well as behavioral indicators. Equipped in this fashion, lenders can reward highly rated borrowers with special offers and take remedial action to keep low-rated borrowers on track.
AI powers key performance indicator tracking that helps originators, underwriters, servicing officers, and collectors zero in on the most important outputs relative to their responsibilities. These evaluations use AI to process, cross-reference, and present the data to the management in report form.
Just as alternative data points can show a supposedly high-risk applicant to be a good risk, after all, permission-based geotracking linked to a mobile device, wifi, IP, or physical address can help set lenders’ minds at ease. Displayed as insight-rich heatmaps, lenders can track borrowers and their financed assets for follow-up or recovery.
Does your business need a robust analytics tool to help stakeholders evaluate performance? TurnKey Lender’s Business KPI dashboard combines data from your lending operation in a one-view interface for deep and ongoing insight at the loan-portfolio level.
This standalone app enhances TurnKey Lender’s AI-powered Decision Engine with a psychological design by behavioral experts with input from lending specialists and AI engineers, Coupled with deep neural network analysis, the evaluates loan risks and potential borrowers even without credit history or bank account data.
Boiled down, TurnKey Lender’s AI-enhanced delivers a five-point value proposition.
- Machine learning and deep neural networks tailored to your business and customers
- Fastest possible time-to-market
- Market-leading, ready-to-use unified lending management solution
- No-code API integration with any third-party or internal data source or service
- Bank-grade automation at a fraction of a price
“Digital lending is on the rise because it gives organizations — everything from banks to medical practices — new avenues for providing financing and almost limitless flexibility in when and how to do that,” says TurnKey Lender’s Voronenko. “But under the hood, it’s a complex process of collecting, interpreting, and processing vast datasets to reach conclusions that shape credit decisioning and underwriting functions, traditionally the most time-consuming and critical aspects of lending. Our proprietary AI makes all that a lot easier.”