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Personalized Banking: How a system's eligibility model matches customers with perfect Financial products
A fintech revolution which made financial services and products accessible to 4.4 million underserved population.
ROLE
Product Designer
YEAR
2021
PROJECT DURATION
45 days
TEAM
2 Founders
1 Product Designer
1 UX Designer
4 Product Managers
12 Software engineers
3 Data engineers
CONTRIBUTION
Survey protocol, Focus groups, Analytics review, Wireframes, Mockups, User testing, High fidelity prototypes, User accessibility and functionality testing.
I contributed as a product designer, focusing on data-driven approaches and strategizing user experiences, shipping features with agility. These efforts played a pivotal role in defining product-market fit, making 40+ financial products/services accessible to 4.4 million underserved population.
For better understanding, go through part-1 in a series of case studies
2
You are here
How a system's eligibility model matches customers with perfect Financial products
🔽 Reduced Turn around time to 2.5 minutes from days of struggle
🔽 Reduced user drop off rate by 56%
1
How a system connects remote communities with Financial services using CRM
Integration of prepaid credit card in the CRM to help population with no/zero credit history
3
A glimpse of the final solution before you dive in
Journey-1
Users can add customer details to generate referral links for products.
Journey-2
And it displays which products they are eligible for, where users can earn for each referral
User can choose any product to refer
Once the user adds the customer details, the eligibility is displayed.
What's the problem with old flow?
Users will create a customer profile with just their name and mobile number
and it will display a generic list of products for referral and earning.
Let's see what's the main challenge in the background
User completes the sale
Organization finds the lead
Reports to the bank
Bank verify and approves
User will be incentivized
Bank rejects multiple applications due to...
1. No or low credit history
2. No supporting documents
3. No active income
& many other reasons
To understand the challenges, I did...
Focus groups
Stakeholder interviews
Analytics review
The main pain point for the user is the turnaround time (TAT) to complete the task, which spans several days
- collaboration with product team
Since 90% of our user base comes from less tech-savvy communities...
They never go through terms & conditions
Hours of training haven't yielded the expected results for the company
Despite a growing customer network, users were unable to convert leads
An average user is loosing 7 in 10 customers
- report from Data & operations team
Ideating with product team
Solution-1 ❌
A toast popup where the user must agree to and confirm a few details about this customer.
Solution-2 ⏰
For the time being, we have gone live with the 'Recommended feature,' which suggests users based on the customer's mobile number.
Checking customer eligibility would solve the issue and streamline the user journey.
Before
Open app
Choose category (Credit card, Loans etc,.)
Choose product (Discover, Amex, Kotak etc,.)
Select customer for application
Share referral link
Track lead
Earn for successful application
After
Open app
Choose category
Add customer
Choose product
Select customer
Check eligibility
Enter few details
Share referral link
Track lead
Earn for successful application
Collaboration with Founders, Data, Product and Engineering led to integration of "Experian's API" in the system
Collection of social proof information to verify identity
To suggest products based on location as some banks should have physical branch to provide service
Product suggestions based on annual income
Product suggestions based on employment type
Product suggestions for bank accounts and money wallets
Product suggestions for vehicle insurance and loans
And here's the final design of eligibility model
Add customer to check eligibilty
Check customer eligibility for specific product
Supporting my engineering friends to go live so that we can proceed with our planned trip :)
2 am in the midnight, while it's raining heavily!
Minute-by-minute user accessibility testing, second-by-second user functionality testing, and some good food...
Finally, we went live. 🚀
How it performed: Few impactful metrics that I'm always proud of
Days
2.5 Minutes
Turn around time (Tat)
🔽 56%
User drop off rate
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