Tag Archives: Decision Optimization

Collections & Recovery Collection Optimization At Shop Direct = Loyal Customers (video)

Collection Optimization Helps Keep Loyal Customers
Sep032018

Earlier this year at FICO World 2018,  we held a session with Shop Direct (as one of our FICO Decision Award Winners) on their journey with collections optimization. As an online retailer, Shop Direct decided to go beyond rules and scoring in improving debt collection and rehabilitation. They wanted to “take it to the next level” increasing collections while reducing expenditures. They turned to sophisticated but easy-to-use decision optimization to supercharge champion/challenger testing, and empower business users to apply statistical science to balance multiple constraints and ultimately boost results. Check out this short video where Mark Harrison-North, Head of Risk Strategy, Shop Direct explains why collection optimization was a priority for the online retailer. Collection Optimization Helps Keep Loyal Customers – Transcript:  Mark Harrison-North, Head of Risk Strategy, Shop Direct What makes Shop Direct unique? Shop Direct’s USP is it’s an online department store, the second largest online pure-play retailer in the UK.... [Read More]

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Analytics & Optimization Modelling Deposit Price Elasticity: What Data Do You Need?

Analytics on screen
Aug142017

This is the second in a series of blog posts on deposit price elasticity, focusing on the modelling data requirements. There are several different modelling techniques and approaches to measure deposit price elasticity, which is dependent on the actual business problem and model usage. The exact data requirements might need to be amended to account for the modelling technique, but a large number of data items are consistent across all approaches. As with all modelling projects, it is good to initially take a step back and think about what type of information you would expect to be predictive, impact deposit price elasticity, and make you move your savings across different products and / or financial institutions. We would suggest that considering the below fields would be best practice for a deposit price elasticity model development: Product details: Historical interest rate of the product(s) to be modelled, this would include retention... [Read More]

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Analytics & Optimization Modeling Deposit Price Elasticity: What Is It All About?

Chart showing components of FICO deposit price optimization solution
Jul312017

Many top financial institutions have begun using predictive modelling and optimization to improve deposit pricing. This requires an understanding of customers’ deposit price elasticity — how sensitive are they to pricing changes, and what is the relationship between price and demand at the customer, segment and portfolio level? I’m going to explore this topic in a series of posts, which should be useful both to deposit portfolio managers and analytics teams. To start with, let’s look at the basics. Price elasticity is the study of responsiveness, and how the demand of a product changes with respect to price (and/or the price of competitors). Understanding deposit price elasticity, or having models that predict this, means you can quantify: Impact of a product’s price change on the deposit product How competitor price changes impact a deposit portfolio Impact of changing macro-economic conditions, such as a change in central bank lending rate How... [Read More]

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Collections & Recovery FICO Optimization Helps Toyota Keep Customers in Cars

Toyota logo
Nov172015

In a recent post, my colleague Martin Germanis talked about the importance of optimization in collections. Toyota Financial Services has put this into practice with dramatic results. Toyota’s Collections Treatment Optimization program integrates decision management, reporting and advanced analytics to provide a data-driven, scientific and customer-centric approach to collections. During its first year, the program helped more than 6,000 customers avoid repossession and stay in their cars, and prevented 50,000 customers from reaching a stage of delinquency that would affect their credit. “Working with delinquent customers to keep them in their cars while working out payment options has helped Toyota avoid millions of dollars in losses,” said Jim Bander, national manager for decision science at Toyota Financial Services. “It’s a win for our customers, and a win for Toyota. Furthermore, it reduced our operating expense ratio by allowing Toyota to grow our portfolio by roughly nine percent, without adding collections... [Read More]

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