Driving Profitability by Bringing AI to Consumer Lending

August 23, 2019

Lenders looking to boost profitability in this tough environment are turning to Artificial Intelligence (AI) given its ability to rapidly assess borrowers’ creditworthiness and cost-effectively mitigate risks. Here are three ways AI can transform lending.

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In addition to traditional challenges such as falling interest rates and operational risks, modern lenders are faced with the threat of increasing fintech competition and rapid digital disruption. Add to it the problem of low-performing loan portfolios and you have a conundrum of sorts.

Lenders looking to boost profitability in this tough environment are turning to Artificial Intelligence (AI) given its ability to rapidly assess borrowers’ creditworthiness and cost-effectively mitigate risks. By processing enormous amounts of data that traditional analytics programs are incapable of handling, AI enables lenders to compress costs, widen reach, and reduce lending cycle time. Amazon, for instance, collects huge amounts of consumer data on the products sold on its site and how consumers feel about them. Using machine learning, it then analyzes the data to identify credit worthy small businesses that make the popular products. Using this approach, in 2017, Amazon lent nearly USD 1 billion to small businesses that use its marketplace.

Here are three ways AI can transform lending:

Determine credit worthiness beyond traditional scores: Accurately determining how likely an individual is to default is a critical capability for lenders as it directly impacts their profitability.  Most lenders use statistical methods of evaluation to determine creditworthiness based on approximately 20 data points - a process that can often overlook otherwise credit worthy customers. AI and Machine Learning (ML) algorithms enable deeper data analysis by taking into account a variety of aggregated and unaggregated sources, and deriving patterns to uncover meaningful insights. Smartphone data, for example, can reveal information about a person’s willingness to repay. Mobile wallet data, on the other hand, can reveal information about a person’s ability to repay.

Widen reach to include the unbanked population: Lending to micro and informal businesses, self-employed, low-income/remotely located households, and even millennials and students, is challenging due to reasons such as lack of credit history or steady income. This presents a huge untapped opportunity for lenders as the market size of the unbanked segment is growing rapidly.

AI has the potential to change this equation as machine learning algorithms can read and analyze real time mobile data to derive highly-predictive behavioral insights into potential customers. AI-based credit-assessment methods allow more people in areas without bank branches to open accounts online and apply and access loans/credit cards at fairer terms compared to informal and predatory lenders. FundKo, a secure online peer-to-peer lending platform leverages AI algorithms to connect lenders and investors with qualified borrowers (including small and medium-sized businesses) at lower rates and flexible terms.  

Compress operational costs and time: By using user testing to optimize the loan onboarding process and verifying personal details from images of identity cards, AI drives several benefits. It improves the sales funnel, increases conversion rates, and eliminates the cost of manually processing thousands of applications. The result: reduced lending cycle time and enhanced profitability. GoBear, for instance, harnesses the power of AI to simplify the search, shortlist, scoring and confirmation process for both financial institutions and customers. This helps customers choose the right lender and complete an application in less than five minutes with the Easy Apply app.

On the road to building better risk models

The days of relying solely on traditional socio-demographic data and manual processing in lending are clearly over. According to Gartner, market conditions for commercial success with AI are well aligned, making AI safe enough for CIOs to investigate, experiment with and strategize about potential application use cases.  Lenders that choose to digitize their businesses will have the ability to deliver better customer experiences than ever before while including more unbanked and underbanked in mainstream financial services. AI’s ability to handle raw and unstructured data, identify hidden dependencies and manage arbitrary complexities, gives lenders the ability to build better risk models and unleash the untapped potential of their business.