RIHUB | Ai powered Loan recovery software

For the design process, I followed 4 non linear critical steps.

[Empathize] → [Problem & Goal Definition] → [Ideation] → [Prototype & Testing] → [Iterations]

Too many loans to handle, while you're still doing the old defaulters, there's a high number of new defaulters, human agents are constantly stressed out with the stress of reaching out to old and new defaulters and documents screening before approving loan, thereby customers are being lost in the process and money too because it's hard to keep up with all loans at once

The Goal is to make the process of loan recollection & approval autonomous, thereby allowing human focus on high risk and long time defaulters or customers that have gone into legal stage.

To ensure our design decisions were grounded in real-world data rather than assumptions, we did a comprehensive research phase. Our focus was to gather actionable data directly from the people experiencing the problem daily.

To solve the problem I engaged series of loan recollection officers in series of user interview sessions, and proceeded to design user personas to easily understand the data received.

To validate the market need for RIHUB, we analyzed three standard approaches currently used in the finance industry for loan recovery and management. We evaluated them based on Autonomous Outreach, Smart Triage (Human Handoff), Agent UX, and Approval Automation.

1. A massive portion of daily agent workload is spent on borrowers who are not actually trying to default. Up to 65% of missed payments are due to simple forgetfulness, expired cards, or temporary technical issues.
2. Agents open 3 to 4 different apps to view payment history, read emails, and open PDF documents.
3. Managers lack real-time visibility into collection speed and agent efficiency.

I tailored the solution data from the Research insights. It includes:

1. 100% Autonomous Outreach: AI recovers low-risk accounts automatically in the background.
2. Cleaner and more unified Workspace
3. Real-Time Data visualization

1. Projected to accelerate Collection Velocity by 40%
2. With AI handling up to 70% of routine follow-ups and payment reminders, financial institutions can scale their loan portfolios without needing to aggressively expand their human call center staff.
3. Projected to drastically Reduce Agent Burnout by 60%
4. 60% Faster Case Resolution

1. Customers get offended at ai electronic voices, the wait, latency issues etc
2. Customers don’t have a problem with being responded with ai, they hate spending too much time trying to resolve basic issues or agents not having enough information.

Ai called at ungodly hours and continued calling customers even if they are unavailable, so we created a way to set them to not call past a time and no of calls before defaulting to a text The new screen is attached below