Tag Archives: Retail Banking

Analytics & Optimization Modelling Deposit Price Elasticity: Challenges and Approach

Analytics on screen
Sep042017

This is the third in a series of blogs on deposit pricing, focusing on price elasticity modelling approaches and challenges. The goal of any deposit price optimization solution is to make data-driven pricing decisions to manage portfolio balances and trade these off against the associated costs. These solutions should allow a pricing manager to prepare and run what-if analyses to assess the impact of pricing strategies, competitor price actions or movements in central bank base rates. Fundamental to these solutions are price-elasticity models that capture and predict customer behavior as a response to pricing and other non-price factors. In this blog, we discuss the challenges and solution approaches for the development of robust price-elasticity models. Price Response Signal Price sensitivity can be measured with regards to product rate, market ranking, competitor rates or even interest paid to other products in the portfolio. The modelling challenge is not only to measure... [Read More]

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Fraud & Security PSD2 – Why is Transaction Risk Analysis Important for PSPs?

PSD2 with question mark
Sep042017

Worried about increasing levels of fraud, particularly in remote payments, the regulators have made fraud prevention a cornerstone of PSD2. The regulated use of Strong Customer Authentication (SCA) by payment service providers (PSPs) to secure payments is laid out in the Regulatory Technical Standards for PSD2. However, the use of SCA to secure every payment over €30 could cause problems for PSPs. It could impact the level of customer service they can offer by forcing them to add friction to the consumers’ payment process, forcing consumers to re-authenticate themselves using multiple factors at the point of payment. There is a difficult balancing act between fraud reduction and customer experience. PSPs will be allowed to manage this balance by securing payments using transaction risk analysis (TRA) – as long as they can keep their fraud rates low enough (see the transaction value table from the regulatory technical standard, below). TRA is... [Read More]

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Fraud & Security Double-Digit ATM Compromise Growth Continues in US

ATM with Hacked stamp
Aug312017

While data breaches and ransomware grab the headlines, we’re still seeing fraud growth due to ATM compromises in the US. The fraud growth rate has slowed down from the gangbusters surge we saw in 2015, but consumers and issuers still need to pay attention. The latest data from the FICO® Card Alert Service, which monitors hundreds of thousands of ATMs and other readers in the US, shows a 39 percent increase in the number of cards compromised at US ATMs and merchants in the first six months of 2017, compared to the same period in 2016. The number of POS device and ATM compromises rose 21 percent in the same period. Beyond the numbers, at FICO we have seen the rate of fraud pattern changes accelerating over the last two years. As criminals try to beat the system, we are continually adapting the predictive analytics we use to detect compromises.... [Read More]

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Collections & Recovery Collectors: Don’t Let IFRS 9 Blindfold You

IFRS 9 on blindfold
Aug302017

There’s a clear pecking order when it comes to the IFRS 9 accounting standard that goes into effect in January. It’s an accounting standard, not a piece of banking regulation, so the hierarchy is Finance, Risk and then Collections. This makes sense, but for debt managers it will cause problems. It’s likely that many debt managers will be blind next year on how they can influence impairments. Here’s how things will happen. Your Finance team will talk to your organization’s accounting firm and auditors, and they will agree how IFRS 9 should be implemented. They will probably work your Risk department when it comes to preparing the predictive models that are required to determine expected loss under IFRS 9. Once that’s done, the rules for your organization will be binding. It’s unlikely that the Collections team will be part of the process. And if you’re in Collections, you might think... [Read More]

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Risk & Compliance Will CECL Be a Plus or Minus for Your Competitive Position?

Starting line of race
Aug292017

Whenever there’s a major change in standards looming, companies subject to it understandably go into heads-down mode, focusing on what they need to do to become compliant. Often, there’s an enormous challenge just getting to the start line—the point where the change is required standard practice. For help with reaching the starting line for CECL, the new current expected credit loss impairment model in the US, check out FICO’s just-published CECL Hot Topics Q&A. My colleague Lynda Woodward and I answer questions such as: What is the biggest difference in the change from incurred loss to expected loss? What if we don’t have sufficient data to estimate lifetime losses? There’s a lot of talk about increased volatility in allowance estimates under both CECL and IFRS 9. What are the main causes? Are there some hidden implications of CECL for customer experience and relationship building that I should be considering early... [Read More]

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Analytics & Optimization Using Alternative Data in Credit Risk Modelling

Aug292017

“Whenever I bring up the topic of alternative data, the first question our board asks is, ‘Are we using Facebook data?’ “ This comment from a participant in our recent EMEA Risk Leadership Forum caused a lot of chuckles and nodding heads. When it comes to evaluating credit risk, everyone wants to know if, when and how lenders will start probing their Facebook account. For reasons that will be obvious to lenders, that tantalizing possibility doesn’t actually top the list of data sources to mine. In fact, at the forum we explored a few sources of data that can add to the picture of a consumer’s creditworthiness. Multiple Types of Alternative Data What is alternative data? In credit granting, it generally refers to any data that is not directly related to a consumer’s credit behavior. Traditional data usually means data from a credit bureau, a credit application or a lender’s... [Read More]

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Analytics & Optimization Why We Need Explainable AI (Video)

Box stamped AI
Aug152017

As artificial intelligence reaches new areas such as risk management in banking, explainable AI will become more important. That’s the message FICO’s Derek Dempsey gives in a video interview recorded with Compare the Cloud. Derek, a principal consultant with Fair Isaac Advisors who was named one of the Big Data 100 by DataIQ magazine in 2015, discusses the impact AI and machine learning have had in fraud detection, and the growing use in financial services. Risk management has greater requirements for explaining decisions, Derek says, and this will force AI developers to think differently. For more on this topic, read Scott Zoldi’s blog posts on explainable AI. Watch the video on Compare the Cloud

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Fraud & Security PSD2 Glossary – the 50 Terms You Need to Understand

PSD2 logo
Aug072017

It’s not unusual for EU Directives to arrive with a whole host of acronyms and terminology, and PSD2 is no exception. From AISP to XS2A there are some new terms, some terms that have a new meaning in this context, and some established payment terms it’s always worth having a reminder about. When I couldn’t find a glossary to help me understand the terminology, I decided to create one. Or the 50 PSD2 terms, here are my 3 favorites: ASPSP – Account Servicing Payment Service Provider — a tongue-twister to rival Sister Suzie and her shirt-sewing shenanigans! An ASPSP is a Payment Service Provider (PSP) such as a bank or card issuer that provides authorised access to bank account information. For PSD2 they are allowing API access to bank account data for Account Information Service Providers (AISPs) and Payment Initiation Service Providers (PISPs). This makes my top three as I can’t... [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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