Case Study

An AI-powered API Analytics Platform Built on Databricks

A reputed national bank gains real-time insights into the API performance of its online banking platform for customer excellence

A nationalized bank in the US leveraged a sophisticated AI-driven API analytics solution built on Databricks to optimize the API performance of its online banking portal and transform the user experience.
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Industry Stack :Banking and Financial Services, USA
Tech Stack & Techniques:
Cloud Computing Platform: Azure, Data Platform: Databricks, Data Visualization: Power BI, Machine Learning Platform: Scikit-learn, Programming Language: Python, Predictive Analytics: Time Series Forecasting, Application Performance Monitoring (APM): Azure App Insights
Client Overview
Our client is a major national bank with a large online banking platform that allows customers to access their accounts, pay bills, sign up for banking products, and transfer money among various other banking services through their website and mobile app.
Business Challenge
The bank was expanding its business and wanted to ensure that its technology infrastructure was robust enough to meet the growing digital banking needs of the new-age customers it had been targeting.
Although they were using an API management system for their online banking portal, their existing API infrastructure was limiting business scalability. Their business goals required them to fix all the API-related gaps to strengthen their online banking system and ensure it delivered a seamless customer experience for improved business revenues.
Solution Offered
To enable the bank to overcome its current API-related challenges, KANINI proposed an AI-powered API analytics platform. Based on the proof-of-concept, our team built a fully customized solution on Databricks and leveraged a range of advanced analytics tools, machine learning algorithms, and NLP techniques to offer the bank real-time insights into its APIs – including API performance, usage data, errors, security gaps, and scalability options. Here’s how our AI-powered API analytics platform streamlined API performance –
1. Analyzed API Logs and Provided API Performance Trends

As the solution was built on Databricks, it allowed seamless ingestion and processing of API logs in real-time. Data engineers could extract all relevant information on the API activity such as API traffic, recency, frequency, response time, error rates, cost of API calls, and other such KPIs to obtain the API performance trends. Further, this API activity data gathered over time could be visualized through interactive graphs, charts, and infographics using Databricks Notebooks for actionable insights around API performance. It helped them to make decisions about reusing APIs and identify areas of improvement.

2. Classified and Clustered APIs Based on Log Data
Databricks integrated with ML libraries and frameworks to effortlessly classify the APIs based on behavior, usage patterns, and other performance properties. Similar APIs were then clustered to help the bank easily identify API clusters with similar performance concerns and ensure prompt resolutions.
3. Forecasted API Failures and Other Related Predictions Based on Past Trends
Databricks allowed the training and deployment of machine learning models at scale. We used time series forecasting techniques to predict API failures based on past trends. A deep learning-based AI model was used to train the solution on the historic API log data and offer these proactive insights to address potential API performance issues and ensure seamless customer experience on the bank’s online portal.
Value Delivered
  • Enhanced operational efficiency and customer experience by leveraging Databricks’ data exploration and visualization tools to gain insights.
  • Minimized the risk of malicious API requests through real-time API performance monitoring, management, and troubleshooting in a secure API environment.
  • Allowed scalability of the analytics infrastructure as and when needed.
  • Enabled solution adaptability to fluctuating API log data processing requirements through seamless handling of large data in diverse formats.
  • The integration capabilities of Databricks helped the bank establish a collaborative environment for innovation and business growth.
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AI-powered API Analytics Platform Built on Databricks