Increase/Decrease of the overall Payment transactions of customers during COVID-19. It was very important for any bank to analyze the customer demography and behavior to foresee any risk associated to a given customer and how can a bank mitigate that risk. That inspires us to think about the idea related to Risk Alert, Customer Behaviour Analysis & Loyalty Index.
What it does
It analyze the Customer demographics based on 4 high level factors given below.
• Psychological • Personal • Social • Geographical
- Customer Demography details like Customer/Account Details, Transactions details, Collateral details
- Trend Analysis of Payments done by the customer Pre-Covid/Post-Covid, Monthly/Quaterly/Yearly. 1- Yearly Transaction Views 2- Business Customer transactions vs its Business Sector Index 3- Business transactions vs Geographical Growth Index – which defines Regions Financial and Political stability. Collateral Indicator:
- Existing Loan, Credit History, , Mortgages, Financial Spread of the Customer is derived from the customer profile.
- Additionally we can perform the trend analysis various collateral held by the customer like Forex and stock collateral Based on the reward point received from the above Customer demography and Behaviour analysis trend, that will be the driving factor for customer loyalty program. Bank can entitle various benefits to the customer or upgrade the customer as a priority customer.
Now by considering the Loyalty index the bank can set up transactions fee band dynamically for a given customer.
Demonstrating Few additional feature on Transaction fee setup
1- Dynamically customer transaction fees setup , Fees slab and etc. 2- Dynamically build the fees breakdown structure. 3- Keeping eye on the customer business demography and ESG as a parameter for the fee bands. 4- View existing customer Fees break down details.
Also, based on the Customer behavioural trend analysis if the Loyalty index is reducing significantly, this can alert as a risk and the dynamically update the fees slab/band to mitigate the risk.
How I built it
This Prototype is built using Java, React, SpringBoot, Rest Api and Canvas JS.
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Customer Behaviour Analysis & Loyalty Index
This application can be embedded easily with the checks like Credit History, Loan History and Mortgages history Forex forecast indicator displayed to the customer prior to the payment made if he/she a loyal customer. Can be integrated with Open banking AISP (Accounting Information Service Provider) to retrieve account data and build statistic on it. Analyse the customer behaviour based on sanction history of that customer.
Additional Feature and Enhancement can be adopted on the basis of the given Bank's Business Requirements.