The problem Finanzi solves

This is a Machine Learning centric project. So, we have trained different ML models on four datasets and deeply analyzed all the data from the AA to come up with 4 major features that might help stakeholders(banks, business, etc.) as well as the customers. The 4 features we are offering are briefed below:

• Customer Segmentation: It allows banks, businesses and relevant stakeholders to tailor their products and services to each customer segment's specific needs and preferences based on several data points collected through AA. Taking BoB as an example, if a customer is a high-spending user, then we can recommend Baroda Salary Premium Acc.

• Loan Defaulter Detection: This feature helps banks in detecting potential defaulters by going through their financial history. Currently, the process is done manually by humans, but now with the help of AA all the Account histories are available in one place, we apply our model to predict if the customer is eligible for a loan or not. This process is more automatic and accurate with a lot more data

• Cashflow Analysis/ Purchasing Power: This is a feature aimed at customers who are aiming to achieve their financial goals. We analyze their transaction history and show them the cashflow chart such as: Food, grocery, healthcare, EMI, etc. This analysis also helps us in determining in what we call Purchasing Power. It is a score given to people who don’t have a credit history based on their financial data. Right now there are many Buy now Pay Later apps such as Slice, Jupiter, etc. that give out credit with almost no basis. Instead, Purchasing Power Score will help them determine an appropriate credit amount to be given to the customers

• Fraud detection: Trained on a flagged fraud transaction dataset, our model will be able to detect fraudulent transactions in the history of the user, this can help in marking them as a person of interest as well as denying financial services to that particular person.

Challenges we ran into

1. As data was huge,Data segregation for the application to give solution was formidable challenge
2. Taking into consideration all the machine learning models w.r.t finance was challenging as some of probelms are subjective
3. Usage for API,UI performance and security proved to a hard nut to deal with