Creating a High-Performance Analytics Environment to Optimize Loan Offerings

Learn how a financial services leader established a comprehensive analytical environment.

Data Analytics Success Story

Learn how a Financial Services leader established a comprehensive analytical environment to gain a unified view of data across processes, assess credit risk with precision, optimize loan offerings for its customers, and reduce losses significantly.



Analyzing the behavior of credit portfolios for the purpose of setting interest rates requires the graceful integration of data from a variety of operational activities. Our client — a Financial Services Leader — specifically required a unified view of marketing, underwriting, servicing, collection, and recovery activity.

Utilizing Eliassen Group’s proven data integration methodology and its automated architecture creation, the company established a comprehensive analytics environment to flexibly segment customers, deftly review the repayment behavior, and intelligently establish interest rates.

Business Challenge

Like most lending institutions, our client utilized multiple systems to originate and service its credit products. However, the various systems were not integrated, not easily relatable, and often contained conflicting information. How could the company simplify the integration and validation of information from these disparate systems?


Working with the Eliassen Group’s Analytics team, the company’s IT organization transformed its data collection and modeling strategies, fostered a closer working relationship with their internal business customers, and created a high-performance environment to centralize information and deliver comprehensive analytics.

The Story

Business Challenge: A Swing and A Miss

Our client’s success depended on offering competitively-priced loans to properly cover the expenses and risk posed by its borrowers. 

However, forecasting credit performance required harvesting and integrating data from half a dozen systems.

To overcome this challenge, the company engaged a large consulting firm to construct a data warehouse solution.  Four years and millions of dollars later, the company was still unable to properly identify risk segments within its credit portfolios. 

The interim, manually-built solution suffered from a variety of defects:

  • Complete source data was not properly collected and integrated
  • Data transformations were not accurately nor transparently executed, handicapping user adoption
  • The data model did not present the necessary data elements nor historical performance to support analytical needs
  • No data quality architecture was employed
  • No available metadata for source or target data

The absence of a disciplined development methodology prevented the solution from meeting the company’s analytical needs.


Further, the lack of automated development unnecessarily extended timelines and created inconsistent architectural processes.

The failed solution ultimately resulted in the propagation of rogue data solutions across the company.

Hundreds of incongruent analyses arose which led to haphazard pricing tactics. The company was consequently unable to maintain sustainable pricing and profitability within the riskier segments of its portfolio.

Transformation: Re-Evaluating Batting Techniques

The company sought an efficient, cost-effective means of fundamentally rebuilding its analytics environment. They chose the Eliassen Group Analytics Team to lead this rapid turnaround given our deep experience within the financial services industry, ability to automate architectural development, and proven credit data models.


Eliassen Group utilized a team of multi-dimensional experts in technology and finance:

To swiftly identify, profile, document, and centralize the company’s critical data assets. Eliassen Group then deployed its proven development methodology and proprietary software to rapidly automate the construction of the transformation architecture, data quality processes, and dimensional models.

To ensure speedy adoption of the new data environment:

Eliassen Group also designed and deployed a suite of interactive dashboards to present best-practice analysis of credit liquidation, delinquency migration patterns, and cohort-based loss analysis. Eliassen Group supplemented these dashboards with robust training curricula and on-line, interactive metadata resources.


Results: The Homerun

Reaction to the content, reliability, and flexibility of Eliassen Group’s solution has been overwhelmingly positive. Users who abandoned the initial solution to establish their own data environments elected to return to the improved, more reliable information within the new solution.


The newly-created dashboards also:

Promoted a culture of informational transparency and self-service analysis throughout the organization.

Ultimately, the Eliassen Group data model cleansed and integrated over 1,000 elements from virtually every aspect of the credit lifecycle (e.g. borrower demographics, underwriting attributes, servicing performance, collection activity, marketing promotions, etc.).

This integrated data perspective enabled our client to:

  • Surgically identify the risk segments within its credit portfolios
  • Offer improved pricing
  • Reduce credit losses by 60 basis points

Further, the availability of improved, centralized data and analysis techniques resulted in the reduction and consolidation of over 600 legacy reports.