Artificial Intelligence (AI) and Machine Learning (ML) are having a significant influence on industries. From robotic process automation and speech recognition to virtual agents and driverless cars, the extent of its impact has moved us from a mobile-first world to AI first.
In a recent study of digital executives, the majority, 31%, said, virtual personal assistants following Automated data analysts (29%), automated communications like e-mails and chatbots (28%), automated research reports and information aggregation (26%), and automated operational and efficiency analysts (26%) rounded out the top five. Business leaders said they believe AI is going to be fundamental in the future. In fact, 72% termed it a significant ‘business advantage.’
AI enables enterprises to unleash the trapped value in their core businesses. Machine-based neural networks can comprehend a billion pieces of data in seconds, placing the ideal solution at a decision maker’s fingertips. Your data is constantly being updated, which indicates your ML models will be revised too. Your enterprise will always have access to the latest information, including breaking insights that can be applied to rapidly changing business requirements. No doubt, many FinTech companies have cut down the costs of credit underwriting to find the right customer through Machine Learning applications.
How can AI help Consumer Lending?
Consumer Lending (CL) of all kinds, such as mortgages, autos, credit cards, student loans, etc., is a data-rich environment. We can say, at its core, lending is undeniably all about ‘big data’.
For example, in a typical mortgage lending scenario, we estimate that between the borrower’s credit history, property, employment, income, tax, and insurance information, more than five thousand data attributes are captured during the lending process. This is a time-consuming and expensive process and in case of many lenders, an extremely manual and cumbersome process. And it is difficult to predict how much of this data is even relevant? How much of it is useful in forecasting borrower behavior during the application processing, closing, post funding and servicing stages?
By leveraging more data and analyzing customer default probability, the credit scoring systems can predict behavior, thereby helping lenders come to a more conclusive decision based on data. Fintech organizations need to drill into the insights to grow their business, manage risk, and capture more market share in the competitive consumer lending landscape.
10 ways AI can impact the Consumer Lending industry
Below are just some of the ways that this technology is taking the consumer lending industry by storm.
- Lower underwriting and origination costs by machine
- Reduced credit losses
- Fewer Losses from fraud
- Decreased agency recourse risk
- Better risk-adjusted margins
- Less servicing costs
- Reduced Write-offs
- Greater Customer Satisfaction
- Higher origination revenue
- Lower due-diligence cost
The Road Ahead
It is apparent that AI and ML are the future of consumer lending.
Digital Transformation is drastically impacting the mortgage process, and it is imperative for lenders to stay updated with these changes and adopt them proactively. Technology is no more a roadblock and today’s customers are very receptive to digitalization efforts. Consumers no longer want the same old experience; they want convenient, secure solutions that meet their lending needs.
It is therefore crucial for the lender to create digital mortgage experience that goes beyond an online application to offer a data-driven digital process through AI-powered automation.
AI and Machine Learning have enabled key players across the consumer lending landscape to transform, both regarding their back and front-end processes dramatically. From cost reduction to streamlined operations to increased efficiency, both AI and Machine Learning will continue to pave the way for the consumer lending industry.
The promise of AI has always been to make lives better and to enhance the way we work. AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. Advancements in ubiquitous computing, advanced algorithms, low-cost cloud services, analytics and other next-gen technologies are now allowing AI to flourish. However, AI’s full potential will never be realized until organizations take more risks and begin to experiment with AI technologies more aggressively
Later this month, we will be releasing our white paper on “Reshaping Artificial Intelligence with Consumer Lending.”
