Intelligent Retail Banking

Client Background

Client is an Indian Fintech startup with the goal of enabling banks to provide delightful, relevant & personalized experience to its retail customers. It promises to help customers save more & spend on right things at right price using machine learning & artificial intelligence.

Business Need

Client was in initial stages of conceptualizing the product that would enable it to fulfil its vision. It was sure that this couldn’t be done without application of Analytics & Machine learning but lacked the in-house expertise to do so.

It was therefore desired to engage an analytics provider that would bring in necessary expertise in applying machine learning for personalization & recommendation. Chosen partner needed to work closely with the client in identifying how machine learning could play a role in the product, product features it could power and then go about implementing same. Client already had a technology partner responsible for creating the consumer side of application.

Solution

Client was able to get access to the sample data for credit card customers & their transactions through tie up with a private sector bank in India. Given that client lacked knowledge of machine learning and was unsure of where & how machine learning could be applied, our team had to work closely with the client in educating it and identifying areas in consumer engagement that machine learning can influence. This was more like co-creation of a product.  Following key observations were made from the data:

  1. Credit card transaction data had hygiene issues with non-standardized merchant names. This would impact attempts at discovering consumer preferences for merchants, online & offline.
  2. Merchant categories like Retail, Hypermark, and Entertainment would offer us limited insight into consumer preferences regarding clothing, food preference etc.
  3. A consumer would have specific merchants they would like to shop more often from.
  4. There would be spendings necessary and frequent in nature like grocery, bill payments for mobile, DTH etc. Then there would also be spendings discretionary in nature like entertainment, travel etc.
  5. Client would be onboarding several merchant partners across frequent & non-frequent categories whose offers will be served through the platform.

There were restrictions on SMS based outreach to each customer of 8 per month imposed by the bank. Any recommendation algorithm would need to take this fact into account.

It was decided to:

  1. Enrich merchant meta-data that would allow us to create deep customer profiles and serve relevant offers. Enrichment exercise had to be manual mostly.
  2. Group the merchant categories by frequency of transaction & necessity vs discretionary nature. This would help us allocate the quota of SMS appropriately.
  3. Develop customer spend preferences under categories, merchants, day of the week, time of the day wherever relevant.
  4. Enrich merchant offers with similar tags used for the merchant to enable mapping.
  5. Use collaborative filtering based insights & event-based targeting for driving recommendations initially. Once the system go live we could use feedback to develop more sophisticated algorithms.
  6. Use scalable technologies like Hadoop & Spark for data processing and machine learning so that tomorrow we can scale with larger transaction datasets.

Four months exercise resulted in the development of MVP with modules for data cleaning and transaction enrichment, customer preference creation, rules for event-based recommendation and collaborative filtering based recommendation.

ROI
  1. Allowed client to develop robust MVP within cost constraints and scale it to next level.
  2. Our team also participated in pitching product & its analytics capabilities to private sector banks.
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