Hollywood has always portrayed the future of AI in a dystopian, cataclysmic manner. In reality, I believe things will be different.[Read More]
As artificial intelligence reaches new areas such as risk management in banking, explainable AI will become more important.[Read More]
There are several different modelling techniques and approaches to measure deposit price elasticity, which is dependent on the actual business problem and model usage.[Read More]
To commemorate the silver jubilee of FICO’s use of artificial intelligence and machine learning, we asked FICO employees a question: What does the future of AI look like? The post below is one of the thought-provoking responses, from Shafi Rahman, a principal scientist at FICO, working in Bangalore. The most memorable scene in the film 2001: A Space Odyssey is when HAL 3000 turns off the life-support functions of the crew in an attempt to keep itself from getting disconnected. This was my first encounter with what we call artificial intelligence. Then in Terminator, we were introduced to Skynet, a neural network based AI, which raised an army of robots for self-preservation. Hollywood has always portrayed the future of AI in a dystopian, cataclysmic manner. In reality, I believe things will be different — benign and beneficial to the human-kind. In the last 10 years, we have seen huge revival... [Read More]
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
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]
Arguably, there are more financial institutions located in the New York metropolitan area than anywhere else on the planet, so it was only fitting for a conference on AI, Technology Innovation & Compliance to be held in NYC – at the storied Princeton Club, no less. A few weeks ago I had the pleasure of speaking at this one-day conference, and found the attendees’ receptivity to artificial intelligence (AI), and creativity in applying it, to be inspiring and energizing. Here’s what I learned. CCOs Want AI Choices As you might expect, the Chief Compliance Officers (CCOs) attending the AI conference were extremely interested in applying artificial intelligence to their business, whether in the form of machine learning models, natural language processing or robotic process automation – or all three. These CCOs already had a good understanding of AI in the context of compliance, knowing that: Working the sets of rules... [Read More]
With today’s proliferation of data, digital transformation (DX) has become more than a hot topic: It’s an imperative for businesses of all shapes and sizes. The collision of data, analytics and technology has businesses, analysts and consumers excited — and scared — about what could happen next. On one hand, everyone from banks to bagel shops and travel sites to tractor manufacturers have found new ways to connect the dots in their businesses while forging stronger, more dynamic customer engagement. Artificial intelligence (AI) has come of age in technologies such as smart sensors, robotic arms, and devices that can turn lights and heat on and off, adjust for changes in conditions and preferences, and even automatically reorder food and supplies for us. However, today’s Chief Analytics Officer (and Chief Data Officer and Chief Digital Officer, for example) faces both the promise and precariousness of digitizing business. While significant opportunities abound... [Read More]
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]
That is the question for cybersecurity risk assessment. FICO has been in the analytics business since our inception back in 1956. Our founders, Bill Fair and Earl Isaac, had the novel idea that businesses could make better decisions through data. Before anyone thought to call the resulting algorithms “analytics,” they set off to create game-changing approaches to correlating signals with outcomes to help companies manage risk, reduce expense, and maximize opportunities. Bill and Earl began looking for problems they could solve through an empirical analysis of data, and credit underwriting was a use case that was well-suited to the technique. Most credit-granting organizations had credit applications tucked away in filing cabinets (a source of consistent signal data), and most also had a reasonable handle on outcomes – i.e., who was managing credit to terms and who was in arrears or in default. The ability to relate data known at the time of the... [Read More]
To commemorate the silver jubilee of FICO’s use of artificial intelligence and machine learning, we asked FICO employees a question: What does the future of AI look like? The post below is one of the thought-provoking responses, from Sadat Nazrul, an analytic scientist at FICO, working in San Diego. Looking at the next 20 years, I see us moving well beyond the productivity enhancements AI has brought about so far. With the advent of AI, we will be seeing a renaissance in our own personal lives as well as society as a whole. Healthcare Today, our gadgets have the ability to monitor the number of steps we take, the rate of our heart beat, as well as the contents of our sweat. All this rich information allows a team of doctors, engineers and analysts to monitor our well-being and to maintain our peak performance. Similarly, with innovations in genomic sequencing... [Read More]
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]
PSD2 is on its way but in many respects it feels like it will never happen. So much is still undefined; it’s not yet transposed into national law, the complete Regulatory Technical Standards are not issued and much is still open to interpretation. Given this uncertainty, it is not unreasonable to take a wait and see attitude to preparation – after all it’s likely to be more than a year before you will be expected to apply PSD2 to your payment operations. A laissez faire attitude to preparing your fraud operations for PSD2 may however be a bad move. NOW is the right time to get ready. Read our executive brief Getting Your Fraud Operations Ready for PSD2 so that you can understand the three reasons why you should act now: You will want to secure payments with Transaction Risk Analysis – but that will bring fraud challenges. You will... [Read More]