About 2.5 billion people across the world still lack access to formal credit according to World Bank estimates. An estimated 3 billion people are set to enter the middle class by 2030 and banks in emerging markets are looking at a revenue opportunity of around USD 400 billion in serving unbanked consumers including students, farmers and aspiring businessmen. However, with lenders using asymmetric credit scoring methods for assessment, most people fall outside the purview of the credit ecosystem.
Traditional credit scoring
Historically, credit approval has been based on traditional data sources. These data inputs limit a comprehensive view of the prospects’ credit worthiness, impacting their credit scores. Conventional credit scoring methods use historical credit information requiring the individual to have bank accounts and past credit, thus excluding a vast population.
The Fintech revolution
While financial inclusion has been on the increase, digital is redefining how consumers will access credit, fostering real time credit risk management tools for alternate credit scoring and better management of credit risks.
The Fintech revolution is forging ahead in India and China. A report by Google and Boston Consulting Group (BCG) estimates that digital payments in India will exceed USD 500 billion by 2020, up from USD 50 billion in 2016.
Alternate credit scoring – a boon for the unbanked
Banks and FIs are challenged by legacy systems and archaic processes. Most credit scoring algorithms use logistical regression to compute scores and can handle about 40-50 variables at best, resulting in a lack of comprehensive data for risk assessment. Newer algorithms can multivariate, with the ability to mine, structure, weigh, and use rich data – a key capability in the future of credit scoring.
- Using the power of alternate data sources
A number of new lenders have shown how consumer digital footprint data can be a powerful tool for measuring consumer lending and risk analysis. With a large underserved population accessing the Internet for different requirements, new lending businesses are now able to analyze different data sets to assess the creditworthiness of consumers applying for loans. For instance, Blockchain and biometrics are used in authenticating the identity of a prospect. Other alternative data sources in use globally include online presence, smartphone metadata, psychometric data, social media data, remittance history, e-commerce merchant rating, etc. Utility bill payment pattern is also considered a key indicator of an individual’s propensity for loan repayment. For example, if a person has usually paid utility bills after the due date, this behaviour may imply a higher risk of repayment of the loan.
- Generating digital credit scorecards
Global digital payments is already undergoing rapid change and is expected to grow four times in value by 2020. Most individuals in emerging markets have scant credit history. If they build a digital footprint, FIs can use their digital payment history to sanction credit, as practiced by newer lenders.
In countries like India and China, digital payments are evolving in tandem with the growth in e-commerce. Industry analysts expect the payments boom to lead to a profound shift in consumer lending.
3. Predictive credit behavior with advanced data analysis
Advanced data assessment has made it possible to integrate historical data with new consumer data to assess and predict credit behavior. Data analytics helps convert these diverse data sets to derive relevant and deeper consumer insights.
The advent of Artificial Intelligence (AI) and Machine Learning (ML) has led to a profound shift in how Fintech can shape the future of loan accessibility for consumers and small businesses. Chinese Fintechs are amassing data points and feeding their affiliate banks with valuable consumer data, who are in turn leveraging AI to ‘learn’ more about their consumers to make credit decisions. AI and ML will help in better assessment of consumer data collected, better screening of prospective borrowers and also predict their behavior and spending habits which can help determine the borrowers’ ability and willingness to repay debt. The astute use of advanced data analytics has reduced the volume of bad debts, reduced the timeframe for processing loans and increased overall profitability.
4. Using digital for giving better customer experiences
In the bid to be more customer centric, it is imperative for FIs to leverage digital for personalization and deliver better customer experiences. E.g. With the help of data mining and analytics, a loan origination system can assess the risk level of a prospect and personalize a loan product.
Clearly, advanced technology has a fundamental role to play in helping Financial institutions achieve their digital ambitions and realise the vision of the ‘digital lender’. This vision is reflected in the EPIK’s advanced Loan origination platform “Vanguard”, which enables lenders to digitise their business and profit from it.
Vanguard provides with its Unique and intelligent scorecard giving three different levels of scoring i.e factors, modifiers and cappers helping you give Instant and precise decisions by nullifying errors increasing efficiency and scalability .