How did you come across the idea of a ‘Collection Product’ for NBFCs and BFSI? Was there a need gap? What are the factors that you considered while developing the collection product?
A. The banking, financial services, and insurance (BFSI) industry has been at the forefront of innovation and has undergone a significant change in recent years as a result of the quick uptake of new technologies. However, the industry has also faced its share of difficulties.
Loan recovery is one area where cumbersome manual handling of recovery tasks is still used, despite banks streamlining operations like onboarding new customers, credit risk tracking, and its back-end loan processing through conversational artificial intelligence (AI) solutions.
We saw that apart from a lack of technological sophistication, the Covid pandemic and the tightening of regulatory norms by RBI around using loan recovery agents were proving detrimental to the efficient recovery of loans. Since the pandemic, the default rate in NBFC increased by 2 folds. Additionally, real-time integration and usage of structured and unstructured data are challenging problems being faced by all traditional lenders.
These were the prominent reasons we factored in to ascertain that having an effective AI-driven communication tool to remind borrowers of their delinquency in their preferred language and dialect was the need of the hour. Since Rezo already has vast experience in delivering large volumes of AI-powered voice bots and analytics solutions to a diverse range of industries, developing a Collection product was a natural conclusion and our past experience has come in handy while designing the product.
How is your AI solution mitigating the pain areas of Loan recovery for NBFCs or BFSIs as well as the borrowers?
Conversational AI and analytics are already freeing the NBFCs of cumbersome Manual Handling of loan recovery and making the process more efficient and tactful. AI-driven human-like conversations with the ability to handle multiple languages and dialects have made the process more hassle-free for the lender as well as the borrower.
AI in loan collection analyses every borrower’s profile and payment delay reasons to classify them based on the future risk of missing payments. By analysing the reasons behind missed payments, it can also predict the delinquency pattern of borrowers.
AI can then devise different types of highly innovative and tailored customer conversations and create a unique customer journey for each & every borrower. Our personalised messaging ensures that borrowers are reminded without them being harassed.
What has been the success rate so far for your collection product? And what do you think is the market potential for the product?
The most recent RBI bulletin estimates the year-on-year growth of the deployment of Gross Bank Credit under NBFCs as 35.5%. The conventionally practised trend of carrot and stick in loan recovery has proven to be less efficient and recently controversial as well.
Our collection product’s emphasis on personalised conversation styles based on each borrower’s delinquency pattern is important in this context. Rezo’s ‘Collection Poduct’ has already helped a financial services leader — with a book size of more than $10 billion and a customer base of more than 15 million — achieve up to a 10% jump in collection efficiency.
Our collection product will prove to be virtuous for NBFCs that suffer from often taxing and cumbersome manual handling of loan recovery and recurring default rates.
What are the schematics of the AI-driven conversation with borrowers? (What does the unique customer journey for each borrower based on his/her past delinquency pattern look like?)
The idea was to find a personalised and innovative way to recover loans from the borrowers. We found that borrowers have varied reasons for missing payments – from lack of funds at the moment to forgetfulness to wilful payment default.
Addressing each reason required using a different type of customer conversation and creating a unique customer journey. The Loans collection lifecycle of Rezo’s collection product involves 6 buckets that range from pre-delinquent and early delinquent to Deep Delinquent/NPA. Also based on the buckets targeting methods and strategies change.
Whereas the Pre-Delinquent bucket involves automated nudges/soft reminders to prevent repayment defaults, early delinquent involves quickly resolving unintentional defaults. When the due date gets past by 90 days, the deep delinquent bucket is where our AI-powered ‘Collection Product’ plays the most important role by targeting write-offs and settlements by enabling mass reach-outs on low-success books.
This allows us to target like a traditional collection agency and deliver results leading to better collections and creating a win-win scenario for all. Each customer journey created is designed to ensure that borrowers make the payment at the earliest possible.
Our target is always to prevent anyone of the borrowers become NPA for NBFC or BFSI. This allows us to take target like a traditional collection agency and deliver results leading to better collections and creating a win-win scenario for all.