Data analytics and machine learning in financial services

Solution Accelerators for Financial Services

Based on best-practices from our work with the leading brands, we’ve developed solution accelerators for common data analytics and machine learning use cases to save weeks or months of development time for your data engineers and data scientists.

Modernize Risk Management

Traditional banks relying on on-premises infrastructure can no longer effectively manage risk. This solution helps FSIs adopt a more agile approach to risk management by unifying data and AI.

Data-driven ESG

ESG is widely considered a top-level initiative to achieve resilience and sustainable profitability in a rapidly evolving economic and environmental landscape. This solution provides companies and investors a holistic and data-driven view into ESG performance.


Fraud Prevention with Predictive Analytics

Curbing fraudulent or malicious behavior — from fraudulent securities trading to money laundering — is key to mitigating negative revenue impact. This solution provides ways to leverage data and machine learning to detect anomalies.

Credit Card Fraud with Geospatial Clustering

As consumers become more digitally engaged, large FSIs often have access to real-time GPS coordinates of every purchase made by their customers. In this solution centered around geospatial analytics, we show how the Databricks Lakehouse Platform enables organizations to better understand customers spending behaviors in terms of both who they are and how they bank.

Rule-based AI for Financial Fraud Detection

Preempt fraud with rule-based patterns and select ML algorithms for a successful and reliable fraud detection program, including anomaly detection and fraud prediction to respond to bad actors quickly.


Alternative Data for Investing

Make better investment decisions by revealing valuable insights about trends, behaviors and risks. Alternative data extends across a variety of use cases, including back-testing, market risk and ESG investing.



Enrich transactions with contextual information (brand, category) that can be leveraged for downstream uses such as customer segmentation or fraud prevention.

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