Tag Archives: Analytics

You need to find faster, smarter and more value-inducing ways to get from the data to decisions (and actions) rapidly. Analytics are key to that process.

AI, and more specifically machine learning, are generating enormous buzz in many areas. But are these claims realistic?

“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]

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]

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]

A company that solved an “unsolvable” wind farm optimization problem. An automaker that uses analytics to keep more delinquent customers in their cars. An airline that uses optimization throughout its business to improve customer service and efficiency. These are just some of the past winners of the FICO Decisions Awards – and now it’s time to find the next set of innovators. We’re looking for companies that are achieving outstanding success using FICO analytics and decision management solutions. Awards will be presented in six categories: Artificial Intelligence and Machine Learning Customer Onboarding and Management Debt Management Decision Management Innovation Fraud Control Regulatory Compliance. Winners will receive recognition at FICO World, which will be held April 16-19, 2018, in Miami. Winning implementations will be featured in conference activities, and two representatives of each winning company will receive complimentary conference passes. Nominations are due September 1, and winners will be announced in... [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 Imran Ali, a senior engineer in our Tools Department, working in Bangalore. “I propose to consider the question, Can machines think?” Artificial intelligence has come a long way since that opening statement was made by A. M. Turing in his seminal 1950 paper Computing Machinery and Intelligence. AI has become very popular because it has metamorphosed from being of theoretical interest to something that is creating value in business. To gain further success, AI has to answer an important question that every business has to answer. Consumers will ask, “You are good at what you do, but should I trust you?” Can I trust you with my money to trade with? Can... [Read More]

Although there has been much speculation over this statistic’s origin, most industry experts agree that 80% to 90% of the world’s data is unstructured data, and about 90% of it has been produced over the last two years alone. Of these unthinkably vast stores, only 0.5% is effectively analyzed and used today. In the business world, most unstructured data lies in customer-related text, which is abundant and available. However, most organizations don’t know how to efficiently extract predictive elements from unstructured customer data. They’re not sure how to reap the value of these insights by using them to boost the performance of predictive analytics, and make better operational customer decisions. But, done right, extracting valuable predictive insights from huge quantities of text takes just seconds. The Future Is Unstructured The tech industry is full of predictions, but in this one, I have high confidence: The future is unstructured –– because... [Read More]