76% of Organizations Have Cyber Risk Insurance. Our survey of 500 companies across 11 countries shows some interesting results and attitudes on the subject.[Read More]
This post will address four requirements for getting set for CECL, that is to say, what capabilities your solution must have to provide a stable platform for CECL execution and analysis.[Read More]
At FICO World 2018, I sat down with Chess Grandmaster Garry Kasparov to discuss the hot topics in the world of AI, and asked: Why is explainable AI so important?[Read More]
76% of Organizations Have Cyber Risk Insurance – But How Does That Change Across the Globe? Data breaches and cyber-attacks continue to dominate headlines, so we can be sure that try as they might, many organizations can’t protect themselves sufficiently. Should the worst happen, are companies able to use cyber risk insurance to help them recover? Our survey of 500 companies across 11 countries shows some interesting results and attitudes on the subject. More companies are investing in cyber risk insurance Last year we surveyed companies in six countries, when we compare year on year results we can see that in those countries the percentage of organizations that have cyber risk insurance has gone up from 62% to 75%. Looking at individual countries there are some standout figures. In the UK the percentage of insured companies has leapt by 29% with 90% now investing in cyber risk insurance. The UK... [Read More]
How to get set for CECL In my last blog post, I started to outline my “10 Requirements for an Effective CECL Software Solution,” where I focused on three requirements your organization needs to do to get ready for CECL implementation. This post will address four requirements for getting set for CECL, i.e. what capabilities your solution must have to provide a stable platform for CECL execution and analysis. 4) How to get set for CECL – Seamless process orchestration A robust CECL software solution will orchestrate all end-to-end expected credit loss (ECL) calculations from data input to component model calculations to final reporting and detailed outputs. It will automate processes wherever possible but will also allow manual intervention where required to incorporate management overrides. This orchestration will occur “behind the scenes” but the effects still need to be transparent to the user. All inputs, models, calculations, parameters and key outputs... [Read More]
At FICO World 2018, I sat down with Chess Grandmaster Garry Kasparov to discuss the hot topics in the world of AI, and asked: Why is explainable AI so important?
For many people, owning a home is still a significant milestone in their financial and credit journey. We recently studied the impact of millennials’ student loan debt on their willingness and ability to obtain new mortgage loans. As the housing market continues to remain competitive, we looked at key trends in FICO® Score distributions and default rates for those who have proven they would like to and are able to obtain mortgage loans. The first clear trend observed around newly originated mortgages is that as we get further away from the Great Recession, underwriting criteria seems to have eased and a broader section of consumers are obtaining mortgages as a result. Figure 1 contains a FICO® Score distribution for mortgages opened in different periods between 2009 to 2017. We see that the percent of new mortgage account openings with FICO® Scores less than 750 has climbed significantly in recent years,... [Read More]
When today’s businesses create a list of must-haves in the race to alter and ultimately transform how they operate, machine learning is one of those checkbox necessities. And indeed, when you run the gamut of business-consumer touchpoints, machine learning is, indeed, one of the mainstays. What’s machine learning, and why is it needed? Machine learning is powered by algorithms that learn from data, identifying critical patterns, connections and insights – without being programmed to reach specific conclusions. Machine learning enables more data of multiple types to be analyzed quickly, in order to derive maximum benefit from it. It takes advantage of exponential boosts in data storage capabilities and computing power (at a fraction of the cost than what was previously possible) to help make faster, smarter decisions while constantly learning, evolving and providing unexpected new insights. Where machine learning models are superior to traditional approaches One of the core benefits... [Read More]
The number of UK firms with cybersecurity insurance has risen in the past year — but less than half say their cyber insurance covers all risks. The second annual cybersecurity survey from research and consultancy firm Ovum, for Silicon Valley analytics firm FICO, found that the number of UK firms reporting they have no cybersecurity insurance dropped from 31 percent in 2017 to 10 percent in 2018. While this is substantially better than the 24 percent reported across all 11 countries surveyed, only 38 percent of UK respondents said their cybersecurity insurance covers all risks. Cybersecurity Insurance – Telcos still lack coverage Telecommunications firms were the most likely to have no cybersecurity insurance — 17 percent reported this, compared to just 5 percent of financial services firms. (This shows that not much has changed in the industry since last year as per the findings in our e-book “Cybersecurity for Telecoms –Views... [Read More]
Where in your fraud controls should you be considering post-book controls? And where can you work better with your partners in risk to mitigate losses?
At FICO World 2018, I sat down with Chess Grandmaster Garry Kasparov to discuss the hot topics in the world of AI, and asked: What do you think of ethical AI?
Automation has played a fundamental role in enabling businesses to make better, more consistent and more strategically sound decisions. Increasingly sophisticated cloud-based software has given organizations the capacity to define the policies and regulations that drive decisions, and then to incorporate analytics into models – decision services – that can be refined to account for changing conditions. But software is only the toolset. Decision automation can be effective only as an enabler for an effective process to decide what decisions to support using decision services, and how those services will be used to drive business processes. For that purpose, a proven, documented process is required. FICO facilitates decision service development for its clients using a methodology called Decision Requirements Analysis (DRA), developed by Dr. Alan Fish, Principal Consultant, FICO Decision Solutions. DRA focuses decision-makers’ attention on a domain of decision-making and decomposes it into a network of decisions, which are... [Read More]
Machine learning analytics must be a critical part of a financial institution’s application fraud control strategy.