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]
FICO has just released an interactive infographic on European card fraud trends since 2006, showing that card fraud in the 19 countries studied has hit a new high of €1.8 billion. In the UK, card fraud also a hit a new high in 2016, £618 million, though the rise was less than the rise from 2014 to 2015. To understand what these numbers mean — and what issuers need to do now — it’s important to know how got here. That’s why our map tracks the data back to 2006. Why is 2006 so important? That was the year of ‘love my PIN day on Valentine’s day’ in the UK, as the liability shift between magnetic stripe and chip & PIN technology had been put in place during 2005. So chip & PIN was officially rolled out across Europe and executives expected to see the benefits of that investment in... [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]
The General Data Protection Regulation (GDPR) is a wide-ranging and complex regulation intended to strengthen and unify data protection for all individuals within the European Union (EU). A year ago I blogged about the data governance ramifications of GDPR, and in this blog I’ll focus on another facet of GDPR to talk about a related analytics topic: explainable artificial intelligence (AI). First, let’s start with GDPR. Article 22 of GDPR, “Automated individual decision-making, including profiling,” concerns the use of data in decision-making that affects individuals, such as a person applying for a loan. The regulation says: “1. The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.” Point 2 of Article 22 describes exclusions (including situations involving the person’s explicit consent, such as applying... [Read More]
Economies of scale is one of my favorite economic principles. It’s especially cool to see how FICO customers can realize associated benefits by using our behavioral analytic technology. IDC predicts that in 2017, behavioral analytics across compliance, fraud, and cyber detection and prevention will be in place at 15% of banks, helping them to avoid losses, regulatory fines and sanctions. Banks have already made a big start in the fraud space. FICO introduced behavioral analytics in the early 1990s and we currently analyze two-thirds of the world’s payment card transactions, in real time, for fraud. Now, FICO’s proven behavioral analytics can be applied by forward-thinking institutions to fight a wide range of financial crimes. In doing so, banks can gain powerful technology economies of scale, too, leveraging mature, market-proven analytic models to benefit new domains within their business. How do behavioral analytics work? A quick search may tell you that... [Read More]
Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Given the excitement around AI today, this question is inevitable. It’s also a bit silly. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling — if you know how to use them together. Take, for example, our new credit decisioning solution, FICO Origination Manager Essentials – Small Business. It’s designed to help lenders make faster origination decisions without increasing risk. This new FICO product combines our well-established scorecard technology with AI to build better credit risk models. How FICO Uses AI to Build Better Credit Risk Models As with our other origination products, Origination Manager Essentials includes credit risk models, and these models are segmented — different types of small business customers and different... [Read More]
One of the hottest analytic technologies in mortgage lending is price optimization. This is the application of advanced analytics to pricing strategies, in order to determine the ideal price for every customer that maximizes profitability, given factors such as take-up, affordability, etc. That’s great for banks, but what about building societies and credit unions? If profit isn’t your primary goal — if you exist to serve your members — does price optimization have a place? The answer is definitely yes. Using pricing optimization, building societies and credit unions can develop strategic mortgage offers that target specific objectives, such as customer retention, without hurting the bottom line and ensuring that targets around Treating the Customer Fairly are met. This kind of optimization can really pay off – making an appropriate offer, to the right customer at the right time, typically results in 10-15% higher retention of existing customers. When building societies... [Read More]
Make no mistake about it, I am a technology-loving kind of guy. My job is all about helping people to make better business decisions using analytics technology, and I have boundless enthusiasm for speaking with FICO customers, data scientists and students about the power of behavioral analytics, artificial intelligence and machine learning. But when it comes to my personal life, I’m a bit of a contrarian. There’s been a lot of news about studies showing how too much smartphone use, or too much time on social media, can have a stressing, depressing effect. So I purposefully limit my phone use and I’m not even on Facebook, Instagram, Snapchat, et al. (Although I do love using Twitter as part of my work, to connect with followers all over the world!) How technology can rob joy As the Internet of Things (IoT) matures, I feel like a similar trajectory is starting to... [Read More]
This is a guest post from Thomas H. Davenport and FICO’s Zahir Balaporia. A version of this post was also published on Data Informed. Businesses across many industries spend millions of dollars employing advanced analytics to manage and improve their supply chains. Businesses look to analytics to help with sourcing raw materials more efficiently, improving manufacturing productivity, optimizing inventory, minimizing distribution cost, and other related objectives. But the results can be less than satisfactory. It often takes too long to source the data, build the models and deliver the analytics based solutions to the multitude of decision makers in an organization. Sometimes key steps in the process are omitted completely. In other words, the solution for improving the supply chain – advanced analytics – suffers from the same problems that it aims to solve. Therefore, reducing inefficiencies in the analytics supply chain should be a critical component of any analytics initiative... [Read More]
