Financial Services

Explore Innovative Ways to Engage Customers Through Data & AI Solutions

We hope you enjoy this collection of resources for Banking, Insurance, and Capital Markets. Keep an eye on the BlueGranite Blog for updates on utilizing Microsoft Data & AI for customer analytics.

Digital Transformation in Financial Services

MagGlass

Customer 360

Analytics

Maximize your customer retention by predicting customer churn. Leverage a full-circle behavioral view of the consumer to drive relevant, contextual offers and recommendations. BlueGranite can enhance reporting, provide managers with a list of clients to target with retention efforts, and identify former prospects to work to win back. These crucial insights maximize customer preservation and accurately pinpoint marketing efforts.
TransformIdea

Personalized Marketing

Cross-sell / Up-sell

Create personalized marketing campaigns to build brand recognition and speak to individual target audiences. Drive brand promotion and improve customer experience using Machine Learning and artificial intelligence. Benefits of this type of modern marketing approach include increased open rates, and page views, as well as shorter sales cycle times and lower overall marketing costs.
ProjectionChart

Risk Management

Data Availability

Unify data from across legacy applications and deploy mission-critical compute power to respond faster and more accurately to changing markets. Create long-term data views and manage holdings risk based on history, rather than relying on intuition coupled with scant data. Expand investment options and opportunities, including the increased ability to invest in emerging markets.

Featured Case Study

MODERN DATA PLATFORM FUELS CUSTOMER 360 INSIGHTS, CONSISTENCY FOR LEADING FINANCIAL FIRM

A worldwide wealth management leader is gaining faster, deeper operations insights at locations around the globe by harnessing the power of personalized, expert-led Azure data analytics installments. Through international subsidiaries and joint ventures, this earnings-focused financial services giant brokers retirement and insurance plans.

Meeting unique market regulations and gaining clear views of portfolio performance is critical to client satisfaction, uniform operations, and steady growth, but requires location-tailored solutions.

BlueGranite designed a modern data platform that scales to the needs of each of this financial services firm’s locations.

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KEY RESULTS:

  • Moved 200+ metric calculations to the semantic layer, taking advantage of the in-memory analytical capabilities of Azure Analysis Services.
  • Employs secure, scalable Azure Data Lake Storage and Azure Synapse SQL Pool
  • Ensures a repeatable, flexible ELT framework through Azure Data Factory
  • Allows fast, easy advanced analytics through Azure Databricks collaborative, reliable, high-performance platform.
  • Delivers interactive data visualization and rapid reporting through Power BI.

FINANCIAL SERVICES BLOG POSTS

Explore insightful blog posts from the BlueGranite team covering banking, insurance, and capital markets topics and scenarios.

FINANCIAL SERVICES SOLUTION BRIEFS

Universal Insurer Looks at Speed and Flexibility of Cloud BI Platform

A global insurance giant is lauded by its clients for its custom coverage, product diversity and worldwide presence. The company’s specialized offerings in multiple markets have cemented its foothold as an industry trailblazer.  In an attempt to modernize its operations, the insurer had embarked on a multiyear effort to develop its data warehouse and self-service BI system – one that integrated a large number of different data points from across the entire organization. But as the system had grown, so had its complexity.

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Asset Manager Migrates from Mainframe to Modern Data Platform

For money management firms, making the most of every dollar is critical to maintaining a good reputation with customers. For one global finance leader, this was especially true; from wealth management to life insurance, this firm was directing trillions of dollars in customer assets. To earn — and keep — their customers’ trust, this company needed help finding a solution to streamline the reporting process and cut costs.

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FEATURED RESOURCE:

LOAN RISK ANALYSIS USING MACHINE LEARNING IN AZURE

Given the history of other loans, what's the likelihood of a given potential loan going bad?

When it comes to personal loans, failure is an option. For many financial organizations, this is a serious cost of doing business. According to the Wall Street Journal, private default rates rose in the last quarter to a worrisome 8.1%. Lending today is high-risk, high-reward and only those with the best insights will be able to weather the storm.

Thanks to modern AI, default expenses can be reduced by improved loan risk analysis to predict the likelihood a loan will default. This is better than just using a credit history that evaluates the individual and not the loan.

 

SUMMARY:

  • Showcase a predictive model in Azure Databricks about loan risk analysis.
  • Estimate the probability of default for individual loans.
  • Use real-world lending data to identify which features make a loan most at-risk.
  • Specialize your machine learning training to minimize losses or maximize financial gains.

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