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GOBLIN provides strong blend of cost and functionality with a platform offering predictive and prescriptive analytics

Home > Data Warehousing > GOBLIN > GOBLIN provides strong blend of cost and functionality with a platform offering predictive and prescriptive analytics

May 30, 2023

Data Warehousing

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GOBLIN

We spent a great deal of time in recent months talking to customers and prospects about how we could beef up our GOBLIN 2.0 enterprise and data analytics platform. We asked about new features we could add and how we could simplify configuration options that would provide dynamic, actionable insights to help them make (and share) strategic business decisions across the enterprise.

So what did we hear?

  • They are overwhelmed by the amount of data they have, and they don’t know what to do with all of it.
  • It’s too expensive to create internal infrastructure and hire personnel to organize, store, and protect the data.
  • They often don’t trust the accuracy of the data and spend too much time scrutinizing detail instead of making business decisions they are confident in.

What Companies Want

Most companies in the market for a platform like GOBLIN want TWO things (although they don’t always phrase it this way):

  • Predictive Analytics, which finds patterns in historical data to show what will happen in the future. The models always produce the same outcomes when using the same data. In general, predictive analytics involves building statistical models – often using time-saving machine learning algorithms that can identify and interpret patterns and trends within large datasets and determine the likelihood of any given outcome. They do not, however, make specific recommendations. Rather, the output is something like, Since X happened, you can expect Y to happen now.
  • Prescriptive Analytics, which provides a roadmap of what leaders need to do with very specific, actionable recommendations. This segment also uses machine-learning techniques for a range of problems including risk management and business optimization. The difference is while predictive analytics addresses the likelihood of an outcome, prescriptive analytics leverages additional measures and data to try and measure the interrelated impact of different recommendations.

The strengths of a machine-learning-driven platform like GOBLIN include the ability to consolidate data from multiple legacy platforms; overcome the lack of in-house advanced analytics experience; in-house reporting with the ability to control access to those reports; and more sophisticated options to improve portfolio growth and profitability.

What’s the Difference Between Predictive and Prescriptive Analytics?

Where predictive analytics focuses on using structured historical data such as credit histories, transactional data, and customer/demographic data, prescriptive analytics also uses unstructured data such as free-form speech and text data.

Predictive and prescriptive analytics can help healthcare companies set insurance premiums. For pharma, predictive analytics can be used to determine which drugs are most likely to be successful while prescriptive analytics can help them increase the speed of drug development by identifying which demographics or patient groups are most suitable for a particular clinical trial based on data ranging from age to location and medical history. Used well, companies can reduce the risk of spending large sums of money developing the wrong drug.

We worked with a fintech that wanted to leverage strong customer loyalty to enter the credit-card business. But it had four major system feeds containing valuable performance and customer-identifying data that didn’t talk to each other so it couldn’t unify the data. We were able to meet the client’s predictive and prescriptive analytics needs. After merging the data sets into a series of unique tables that are updated every day, we first built an initial underwriting strategy and then created detailed performance and profitability models, followed by a robust reporting dashboard with automated delivery to designated executives.

Questions to Ask

When talking to possible partners, you should ask how well their platforms work in concert with each other. In our case, the original GOBLIN configuration and the recent enhancements were built to ensure these two approaches complement each other — parts of a unified whole – with neither necessarily more powerful nor preferable than the other. You should also ask if their platform, as GOBLIN does, also integrates descriptive analytics, which provides users with an account of what happened, and diagnostic analytics, which looks at the data to identify the root causes of why something happened.

In our case, GOBLIN has reporting suites for financial-services clients that look at everything from originated loans to credit losses on a vintage and point-in-time basis.  These reports can be viewed by leadership daily to make strategic decisions that ensure their portfolios are performing to their expectations.

On the prescriptive side, GOBLIN has compliance reporting packages that pick up specific data points that violate our client’s protocols. These reports are designed to detect and recommend instant changes so these violations cease immediately.

GOBLIN’s prescriptive analytics functionality can be used by a bank or fintech to suggest how much it can reduce the cost of a product to bring in new customers without impacting profitability. It can be used to determine the most valuable demographics for marketing or assess and deal with risks associated with new or existing products such as mortgages, credit cards, or insurance and the impact that changes to qualification criteria might have on portfolio quality, profitability, and stability and even on customer satisfaction. Finally, some banks have used prescriptive analytics to choose new locations based on market saturation and demographics.

The challenge when looking for a platform that combines these different capabilities is balancing the costs and risks. There are high-priced versions aimed primarily at huge companies with bells and whistles you probably don’t need, and bargain-basement versions that leave you exposed to unnecessary compliance risks. We’d love to talk to you if you want to find a great option that balances those costs and risks.

DAVID LAROCHE

Managing Partner of U.S. Operations

David LaRoche is the managing partner of U.S. operations for Predictive Analytics Group. Our proprietary GOBLIN enterprise data platform helps clients consolidate data from multiple legacy systems, overcome a lack of in-house advanced analytics experience, and identify new segmentation opportunities to improve portfolio growth and profitability.

Managing Partner of U.S. Operations

Mr. LaRoche is a resourceful, results-oriented Executive with over 25 years of financial services experience; emphasizing collections risk management, dialer operations, MIS and reporting analytics, acquisition strategies, loss forecasting, credit policy, account management strategies, portfolio conversions, due diligence, and collections operations management. He also has over 15 years of direct risk management experience, with 3 years of collections line management experience possessing excellent analytical skills and the ability to manage diverse groups in strategies, modeling, collections, dialer operations, loss forecasting/loan loss reserve modeling, financial analysis, operations and loss avoidance.

David started his career in 1997 as a customer service representative for Travelers Bank. Since then, he has held the following senior positions:

  • Director, US Operations for Bridgeforce Consulting
  • Sr. Director and Call Center Leader for American Express
  • SVP. Collections Strategy and IT leader for Washington Mutual

Chief Risk Officer

Dale Hoops has over 25 years of experience within the financial services industry, with a focus on Risk Management, Collections, Fraud, Account Management Strategies, Loss Forecasting, stress testing, and economic analysis.

Dale started her financial services career in 1996 as a part time customer service representative and teller in a small financial center while attending the University of Richmond. Her career has included senior roles at Bank of America, Citi, and MBNA America. She has experience with multiple retail products, including consumer and commercial cards, private label and co-brand, deposits, vehicle lending, mortgage, and home equity. Her key strengths have been identifying opportunities for improvement through business analysis, strategy development, and risk governance.

In addition to her professional career, Dale has extensive leadership experience with non-profits with event planning, policy, budget, and audit management. She is a member of the Board of Directors for the Girl Scouts of the Chesapeake Bay, which serves 8,000 girls in Delaware, Maryland, and Virginia. She is the former President, Vice President, and Treasurer at a local Parent-Teacher Association, former community pillar chair for Bank of America’s LEAD for Women Delaware network, and served on the leadership team for the Field of Dreams Relay for Life event to raise funds for the American Cancer Society.

Chief Data and Analytics Officer

Mr. Ridgeway has over 30 years of experience within the financial services industry, including Risk Management, Finance, Project Management, Compliance, MIS, IT & Operations. He has held senior roles at several of the top 5 Banks, including MBNA, Wells Fargo & Citibank. Dee has expertise in Risk oversight and a wealth of knowledge in the regulatory footprint (CFPB / OCC / FRB) in financial services. He has hands-on knowledge in the strategy world with numerous credit products including: credit cards, auto lending, mortgage and home equity, and unsecured lending. Dee is a co-founder of Predictive Analytics Group, worked as a Senior Consultant for Hoops Consulting, LLC., and owned & operated Mayflower Analytics LLC.

From a Risk Management perspective, Dee has experience in portfolio management in credit underwriting and loss mitigation during several growth cycles and economic contraction periods. He understands the needs and partners well with operational risk, modeling, and loss forecasting risk functions.

Dee is a SME on risk data strategy (data architecture, data management, and systems integration) and often creates a "passable bridge" between Risk and IT that translates business needs into executable business plans.

From an MIS, reporting, and portfolio analytics perspective, Dee has a proven track record of designing portfolio reporting that meets executive and end user needs that often have been labeled the "gold standard."

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CEO and Chief Strategy Officer

Mr. Hoops has over 25 years of experience within the financial services industry, including Credit Collections & Fraud Risk Management, Business Operations, Control &Compliance, Strategic Planning, Forecasting, and Marketing Analytics. He has served as a Chief Risk Officer for Barclaycard US Partnerships, a Global Scoring Head at Citibank, and a Site President for Wells Fargo Financial. Steve is a co-founder of Predictive Analytics Group and has owned & operated Hoops Consulting, LLC for the past 4 years.

Steve started his financial services career in 1993 as a part-time telemarketer while attending the University of Delaware for his Business Administration degree. Mr. Hoops has spent his 25-plus years within the industry building best-in-class operations with each company he has supported. His career has been highlighted by leading several large functions for several Tier 1 and Tier 2International Banks, including:

  • Credit Policy (CRO for $20B co-branded portfolio, Barclaycard US partnership)
  • Credit Policy (SVP for $28B retail Co-brand & Private Label portfolio, Citibank)
  • Loss Forecasting / Loan Loss Reserves ($30B Consumer portfolio, Citi-Financial)
  • Collections Risk Management ($70B Co-brand & Private label portfolios, Citibank)
  • Modeling ($30B Consumer Loan & sub-prime Mortgage portfolio, Citi-Financial)
  • Collection Operations (Head of 410 person operations center, $17B Auto, Personal Loan & Mortgage portfolio, Wells Fargo)
  • Credit Analytics (MBNA/Bank of America, Wells Fargo, Citibank)