The rewarding personal finance manager!

The problem HashCash solves

Problems of the customers:

 No proper way to see all your finance in one place
 Customers not being given the benefit they deserve: - Financial institutions have been having our transactional data since day 1, however they haven’t used them in any positive way for the customers. This is India, and Indians die for offers and discounts. Hence, it is high time to reward them for their day to day transactions.

Problems for the brands:

 Ineffective and Inefficient advertisement and distribution of offers
 Brands don’t have insights about their customers

Our idea can be split into two major parts:

  1. We are designing a reward system business model where a person's daily financial data can help him get introduced to new brands and get good offers at the same time. By analysing a person's daily spending, we are trying to understand one's craze for a brand and in turn, giving him benefits in the form of discount offers as a result of their loyalty towards a brand. Moreover, our machine learning model can recommend a person, specific brands based on the brands he has shopped from in the past. This way brands can attract new customers towards them by providing offer coupons as incentives. It's a win-win for both the parties, users getting discounts on their next purchases, and brands earning new customers.
  2. Our motive is to empower a person with his financial data. People today are scared and unaware of their finance, so our idea also aims at giving them a different perspective on finance. Presenting all the crucial financial info on a minimalistic dashboard will eventually lead to a financially responsible and sound person.

For the future we also have in mind to develop a brand side dashboard where the brands can view all the possible insightful information about their customers such as no. of loyal customers, no. of coupons availed etc. This valuable information can be used to increase their market by deciding suitable offers.

Challenges we ran into

The most difficult challenge we faced was to extract brand/company names from the narration of a transaction. Later after cleaning the data and understanding each type of transaction, we were able to develop a method to accurately extract the required information from a transaction. Also, while setting up the sandbox we came accross some issues which were solved with the help of One Money technical support.