How We Helped a FinTech Improve Analytics-Driven Decision Making by Developing an AI-Powered Platform

~95% reduction in processing hours with the platform
~ 45% increased accuracy in sales and expense
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Analytics Insight predicts the Global Artificial Intelligence market revenue in the BFSI sector will touch a whopping USD48.3 billion by 2025 compared to USD13.7 billion in 2019 growing at a CAGR of 28.6% during the forecast period, 2019-2024.

The banking and finance industry generates an enormous amount of customer data that is to be processed for multiple reports and has zero tolerance for errors. This paves the way for data science tools to be introduced into this space. 

Our valued client offers an intelligent analytics platform for fintech businesses that enables them to automate predictable decisions for reducing manual effort and errors substantially. They solve problems with cross-disciplinary teams, drawn from top-tier research universities and consultancies, possessing industry, data science, and computer science expertise.  

They wanted to build a platform that could assist the BFSI conglomerates to forecast sales predictions, accelerate discrepancy detection by identifying user behavior patterns. 

Highlights:

We built an analytics platform in collaboration with the client’s team that enables businesses across the BFSI sector to get an edge in decision analytics by processing data and reducing hours of calculations.

Requirement

They needed a platform that was configurable and scalable to serve the needs of their customer.

The Challenge

The project required high time-compliance as the business operations were based on this platform.

The Solution

The fruitful collaboration between our team and their experts led to the development of a ready-to-use platform-based solution that is: 

  • Easy to configure, scale, and deploy
  • Industry-agnostic and allows workflow orchestration for any use case
  • AI-ready to automate redundant manual tasks
  • Flexible to configure business compliant data analysis rules
  • Capable of analyzing the input data based on a set of rules
  • Intuitive in highlighting the data anomalies and breaches for audit purposes

Overall Approach

A custom combination of solutions was utilized to create this dynamic and accurate platform. The host of AWS services were utilized for monitoring, logging, assessing, auditing, and evaluating the processes. We used Camunda for modeling and creating complex custom workflows that were tailored to the business domain. Data Analysis services in python allowed us to run Data science checks to detect exceptions.

Tech Stack

  • Tools: Camunda, DataBricks
  • Cloud Platform: AWS 
  • Frameworks: AngularJs(JS) , NestJs(Nodejs) , API Platform(PHP)
  • Deployment: Docker, Kubernetes, Spinnaker

 

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Benefits

  • Efficient resource allocation and removing repetitive tasks led to a reduction in labor hours by 80-95%. 
  • Streamlined internal processing and enabled higher productivity via a collaborative dashboard
  • Quick onboarding and processing of newer clients 
  • Higher Accuracy of ~45% in prediction forecast for their customers 

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