Financial Services · Collections optimization
Banao builds collections optimization systems that score every account in your loan book daily — by propensity to pay, channel preference, and days-past-due band — and route agents to the contacts most likely to resolve, not the queue that arrived first.
The system connects to your existing CRM and dialler, adds WhatsApp and email outreach, and gives supervisors a live ops dashboard and compliance log on every contact attempt. No core replacement required.
A digital NBFC— AI propensity scoring deployed across a 100,000-account loan book with multi-channel outreach.
A collections AI build is not just a model. It is the scoring, the outreach layer, the ops visibility, and the compliance trail — we own all four.
Each account scored every morning on likelihood to pay — by days-past-due band, payment history, channel response, and alt-data signals. Agents start the day with a ranked queue, not a list sorted by account number.
WhatsApp, voice, and email sequenced by channel affinity and prior response patterns, with time-of-day targeting based on historical contact rates. Accounts with a WhatsApp response history go there first; call-droppers go to email.
Commitments logged per account, tracked automatically. A missed promise-to-pay escalates to the next contact tier without a supervisor chasing updates from agents.
Recovery rate by DPD bucket, segment, and agent — updated through the day, not from a batch job run last night. The collections manager sees where queues are stalling before the day is half done.
Every call, message, and override timestamped and attributed, with a full history per account. When a regulator asks for a contact record, the answer is a query, not a spreadsheet retrieved from three systems.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. Clients in this vertical are described without identifying them where contracts require.
A digital NBFC lender
The lender's agents worked a flat dialler queue with no prioritization. High-propensity accounts sat behind low-propensity ones; broken promises went untracked. Banao deployed daily scoring, multi-channel outreach, and a live supervisor dashboard — integrating with the existing CRM without a core change.
Banao operates a ~300-person engineering company on its own AI products before a client sees them. InterviewGod screens our own engineering hires. Vikaas runs our own demand generation. Systems that have to survive our own operation are hardened before they reach your collections team.
For BFSI this matters in a specific way: our internal AI already carries audit logs, access controls, and compliance documentation, because we answer to our own finance and legal teams first.
We will tell you before you spend. Most AI vendors pitch the model first. We start with the problem.
We do not quote a collections build off a deck. We look at your actual book and data first.
We audit your loan book data, contact history, and existing dialler setup, then hand back a propensity model baseline, a channel-prioritization plan, and recovery ROI maths — yours to keep. If you proceed, the Sprint is credited against the build.
Data pipeline first, then the scoring model and outreach layer, integrated with your existing CRM, dialler, and WhatsApp Business API. Compliance log and ops dashboard are part of the deliverable.
Live deployment with daily scoring, a supervisor dashboard, and compliance reporting. The model updates monthly as new payment and contact data comes in — so recovery rates improve over time, not just at launch.
What data do you need to build the propensity model?
At minimum: loan account records, days-past-due history, prior contact attempts and outcomes, and any payment events. If you also have bank-statement or UPI signals, those improve scoring on thin-file accounts. The Discovery Sprint tells you exactly what you have and what it is worth.
Will it connect to our existing dialler and CRM?
Yes. Banao integrates with the common contact-centre platforms — Exotel, Knowlarity, Genesys, and others — via API or webhook. The scoring and routing layer sits on top; we do not replace your dialler. Integration approach is confirmed in week one.
How do you handle RBI guidelines on contact frequency and timing?
The outreach layer enforces contact windows, frequency caps, and do-not-disturb periods as configuration, not as an afterthought. Every restriction your compliance team specifies is a hard constraint in the scheduling logic, with a full log if a regulator asks for it.
How long before we see a change in recovery rates?
Early-bucket accounts typically show a measurable shift within the first billing cycle after the scored queue goes live. Late-bucket recovery takes longer — the model needs payment events to recalibrate. We set expectations at Sprint close, not after go-live.
What happens when an agent disagrees with the priority score?
Agents can override and add a reason code. Those overrides feed back into the model — if a segment of accounts the model scores high consistently fails to resolve, the scoring adjusts. Agent judgement improves the system rather than fighting it.
Bring your loan book size, current recovery rate, and what a one-point improvement is worth. In 45 minutes we will tell you whether AI prioritization is worth building — and what the baseline model would look like.
Book a 45-min scoping call
