Stay on TrAAck: Loan Monitoring for the Future
In India’s rapidly evolving lending landscape, a remarkable transformation is underway. Lending processes are evolving from traditional, reactive & punitive payment-to-payment relationships to a more continuous and proactive approach. This approach is more suited for the unbanked and underserved segments in India. The AA-led innovation high tide is poised to disrupt incumbent processes at every step of the loan lifecycle. Read further for an engaging use case of personal lending in short-term credit cycles presented by Snapmint, Agya AA, and Setu at the SamvAAd Show-and-Tell Session.
What is the Problem?
For a seamless checkout experience, platforms depend on instant, real-time underwriting of the customers. Data from apps, SMS, and other alternative data is leveraged for these processes. But LSPs face a challenge when underwriting expensive purchases by new-to-bank (NTB) customers that lack this surrogate data. At the same time, collecting net banking details and screen scraping to fulfill these prerogatives are intrusive and expensive options, which also lower conversion due to customer distrust. The Account Aggregator (AA) framework solves the problem through a consent manager architecture that enables user data control and granular data sharing.
In the AA ecosystem, consent flows have been predominantly designed for a single use case at a time. This demonstration by Snapmint, Agya AA, and Setu introduced a new type of consent flow. This “dual consent” flow gets consent for different, albeit allied, use cases. Integrating separate consents within a single flow aims to ease customer experience. Since dual consents are new, customers might not be familiar with them. That is why they are offered to choose consent based on what the FIU and the customers want.
|Purpose Code||Fetch type||Insights|
|Consent 1||Underwriting||Single fetch||Customer Credit Profile: end-of-day balances, fraud indicators, willingness to pay indicators, check bounces, irregular salary credits, FOIR, expense summaries, income summaries, income breakdown, etc.|
|Consent 2||Monitoring||Periodic fetch– twice every month||Monitoring Insights: balances in relation to EMI amount, drop in cash flows, indicators of increased probability of default, etc|
As the loan is active, monitoring insights are triggered periodically, twice every month, throughout the loan tenure. By receiving timely signals, lenders can take proactive remedial measures, reducing the need for reactive and punitive actions.
Loan monitoring and proactive remedial measures improve the lifecycle of lending for consumers as well as lenders as a whole. The impact of these changes is substantial, benefiting both consumers and lenders alike. A seamless customer experience results in better limits, increased flexibility in EMI payments, and options for accelerated foreclosures with cash-back benefits. This new lending model leads to increased sales, generates better commissions, and offers a sustainable alternative to deep discounting practices. Snapmint witnessed a 27% increase in revenues, exponential completion rates for products with higher limits, zero bank statement frauds, and a steep drop in loan processing costs.
Value Proposition of AAs
Why would customers use AAs?
|Smoother data-sharing process||Control over data||Cross-sectoral data at the fingertips|
Why would lenders use AAs?
|Solves critical problems in the incumbent processes, such as|
|Consent conversion||User convenience||Privacy risks in data access & use||Statement Fraud||Processing cost|
To watch the demo and the complete presentation by the Snapmint, Agya AA, and Setu team, check out the session recording here: