India’s FinTech adoption rate is the highest in the world, at an impressive 87%. It’s easy to see why, considering conditions have been ripe for a while now. India has 1.8 billion mobile connections, 700 million internet users, and 600 smartphones.
Add to this the superior convenience and speed of digital lending, and you have a clear recipe for digital lending success.
But here’s the thing – despite its advantages over traditional banking, 42% of digital banking consumers abandon opening an online account due to the lengthy application process. Moreover, in January this year, Google recently removed over 2,000 loan apps from India Play Store for violating terms, misrepresenting information, and questionable offline behaviour.
The offline behaviour referred to includes constant phone calls, threatening emails and messages sent as part of the debt recovery process.
This shows that lenders – in an effort to disburse as many loans as possible and as quickly as possible – can sometimes put customer experience on the back burner. No one wants to jump through several hoops to apply for a loan (that’s why they’ve chosen digital over traditional banks in the first place). And among the ones that complete applications and secure their loans, many find themselves facing constant harassment when it comes time for repayment.
Technology as a solution to poor CX across the loan lifecycle
- Lenders are increasingly counting on technology to do two things:
– Simplify each step of the loan application process
– Recover loans both efficiently and humanely
Here’s how both these objectives can be met with a combination of artificial intelligence (AI), machine learning (ML), and big data analytics:
Enabling prequalification to improve the experience
Lenders can leverage various data sources to filter out ineligible borrowers, even before the loan application has begun. Alternative data-based credit risk models paint a full picture of the borrowers and segment them into risk buckets. This eventually helps curb drop-offs at subsequent stages of the lending funnel.
For example, a comprehensive risk intelligence suite evaluates borrowers by accessing user data after securing explicit consent. Data types ranging from basic details such as age, income, and location to financial information such as cash flows, spend patterns, and credit usage to behavioural variables such as risk tendencies, lifestyle, attitude, financial discipline, and decision-making patterns are assessed to segment the customers.
Insights generated from this data are used to weed out ineligible borrowers right at the onset of the onboarding process.
Using adaptive onboarding to shorten journeys
Each customer is different, and their onboarding journeys should be too. Lenders can boost conversions with adaptive journeys that require risk-optimized checkpoints. Users with a higher credit score can be onboarded with fewer clicks as opposed to standard borrowers who must pass through several checkpoints.
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Leveraging real-time intelligence to improve collections
A collections-specific portfolio monitoring model driven by machine learning, analytics, and prioritized segmentation would make for a multi-dimensional matrix that factors in a variety of dynamic parameters. Real-time analytics will help identify those likely to default well in time, enabling a shift from a reactive to a preventive approach.
A collection and prioritization engine powered by AI and ML can take stock of customers’ spending behaviour, quality of credit and utilization. This data helps lenders preempt a default and deploy remedial measures accordingly.
Conclusion
When it comes to financial products, every process involved must be both effortless and ethical – and the right risk intelligence technology ensures both. Data, combined with AI and ML helps lenders disburse loans more efficiently, and recover them without having to resort to scare tactics. This leads to lower NPAs and happier customers!
By: Rajat Deshpande, CEO, and Co-Founder of FinBox
