How is the fight against financial crime, fraud and cyber threats leading to a converged approach within security organizations? FICO CTO Stuart Wells discusses the tools and skills needed for convergence in this podcast interview conducted by Tom Field at Bank InfoSecurity.[Read More]
In early December, in partnership with Kaggle, a platform for predictive modeling competitions, we asked: is it possible to create a job scheduling algorithm that not only meets Santa’s toy target, but keeps his elves healthy and happy? Hear from the winners in their own words.[Read More]
We’re entering the third epoch of Big Data. A few organizations are starting to make Big Data-driven decisions that fuel customer experience, profitability and risk management – all powered by analytic engines that inform what happened, why, what to do next and when to do it.[Read More]
How is the fight against financial crime, fraud and cyber threats leading to a converged approach within security organizations? FICO CTO Stuart Wells discusses the tools and skills needed for convergence in this podcast interview conducted by Tom Field at Bank InfoSecurity.
“It’s a particularly worrisome time,” Wells says. “There’s response from the government, from regulators and financial institutions, but it’s hard to stay ahead of the criminal element. There’s a lot of change, evolution and sophistication in financial crimes, and we have to rise to the challenge.”
In an interview about financial crimes trends, Wells discusses:
The trends behind the convergence of financial crimes, fraud and cybersecurity New tools and skills necessary to detect and fight these crimes Where to get started and earn quick winsLISTEN NOW
“Agile” and “originations” are two words not often used together in the same sentence, except perhaps: “I wish our origination system allowed us to be more agile in adapting to changes in underwriting criteria.”
Agility, defined
Last year, my colleague David Lightfoot wrote a blog about upgrading origination systems to meet rising customer expectations. That post focused on auto lending, but the same core principal – agility – applies to the broader originations market.
Nearly every financial organization in developed markets already benefits from the process automation that modern origination software offers. But automation is not the same as agility. After “Day 1,” when the origination system goes live, the processes that need to be automated don’t change much. But the underwriting criteria on which credit decisions are made do change, on a constant basis.
Financial organizations often have had to code laborious workarounds or been hamstrung for weeks trying... [Read More]
It seems like just yesterday Santa got back from his sunny post-Christmas vacation, and now we’re talking about how Santa’s operation can be optimized mathematically. But, even though the Holidays last a few months, Santa’s operation is a year round affair.
In early December, in partnership with Kaggle, a platform for predictive modeling competitions, we asked is it possible to create a job scheduling algorithm that not only meets Santa’s toy target, but keeps his elves healthy and happy? Can Santa’s workshop be brought out of the dark ages of magic and holiday spirit, and transformed into a highly optimized modern operation?
The defending champions of Kaggle’s annual holiday competition swept all three prizes in the Helping Santa’s Helpers optimization challenge and claimed $20,000. In their own words, courtesy of the Kaggle blog, Marcin Mucha and Marek Cygan of team Master Exploder walk us through their winning approach.
Santa and FICO® Xpress brought team Master Exploder “the... [Read More]
When you think about it, we’re really entering the third epoch of Big Data.
In the first epoch, businesses hustled to invest in infrastructures that could process the volume, variety and velocity of data coming from all directions. This represented a dramatic – and costly – evolution from BI-driven storage paradigms that provided a good look at what happened, but were less useful in predicting “what next” in a transformed world.
In the second epoch, organizations looked for ways to derive value from exponentially growing, diverse bodies of data – providing, of course, they could use it quickly enough so it didn’t become stale. Applications were created to help turn data into decisions, but most were cost-prohibitive to deploy and placed demands on core systems. Also, only... [Read More]
FICO has been investing heavily in cybersecurity analytics and related software solutions, including proof-of-concept partnerships over the last 18 months. This work has met with a tremendous reception as we presented it to prospective partners, clients and industry analysts. Not surprisingly, given our deep expertise and IP assets in fraud, our approach goes a long way to untangling the deeply intertwined problems of cybercrime, data theft and subsequent fraud.
We have just announced our first solution, FICO® Cyber Security Analytics. We have leveraged our formidable arsenal of unique assets — including our streaming data infrastructure, patented profiling technology and self-learning analytics — to create a solution that closes the gap left by today’s cybersecurity solutions.
That gap is illustrated by the chart below. The current approach of gathering data on a compromise, developing a threat’s “signature” and then using that signature to protect against future threats, results in massive time... [Read More]
For almost 30 years, I’ve visited collection and recovery operations all over the world to demonstrate software. I’m still shocked when I see how often operations use a week as a unit to determine the next time to take action on an account. The next steps in workflow are often set to 7, 14, or 21 days from the current date.
And people wonder why they never get in touch with debtors.
My mom plays bridge every Wednesday morning and goes grocery shopping every Wednesday afternoon. She’s never home on that day of the week. If you plan on collecting from her (and I hope it doesn’t come to that), you’ll need to call on a different day of the week.
This is Collections 101: Don’t use multiples of seven for callbacks if you want to recover debt!
It would be great if you could call a consumer every 6... [Read More]
In the world of customer centric targeted marketing, the biggest challenge that marketers face is that of balancing relevance with return on the investment (ROI). Relevance implies that the targeted message speaks to the targeted customer, both in terms of content as well as timing. For example, if a bank reaches out to a customer with a message about taking out a mortgage at an attractive interest rate, it would be relevant only if the customer needs the mortgage in the near future. An irrelevant message can turn off potential customers and can do more harm than good.
Predicting the Right Timing
This problem is a predictive problem where one not only needs to predict the likelihood that the customer would require a mortgage, but also get the timing right. Target the customer too early with a mortgage offer when she is not yet ready for the offer, and it’s... [Read More]
A few years ago, following his success in predicting the results of the 2008 election, poll analyst Nate Silver wrote a book called The Signal and the Noise. The book outlined his approach to understanding the limitations of polling to represent an elections ultimate outcome. By better understanding what and to whom pollsters ask questions, he devised a methodology to focus on polls that, in aggregate, best represented the voting electorate and, more importantly, avoid those polls that historically skewed their results to benefit particular constituencies.
The title of the book best summarized the lesson: Identify the meaningful signals and avoid the noise. This is precisely the problem that Big Data presents to analytic professionals (and non-professionals) today.
In a world where you can collect everything, should you? Probably not. Recently, ZDNet’s Stiligherrian wrote an article in which he discussed the Big Data “collect everything mentality” that grounds many Hadoop... [Read More]
I’ve been blogging about the growing role of analytics in strategic planning. In my last post, I shared a success story from a client that was able to sharpen its strategic vision in order to recognize and avoid a looming profit cliff. In this post, I’ll discuss our work with a bank that, after acquiring a portfolio, had to quickly counter performance trends that could reduce ROI on this asset.
This regional US bank—who I’ll call Bank B—had acquired a credit card portfolio with a prevalence of high-scoring customers, indicating low credit risk. With conversion requiring a lot of staff and analyst time, the bank asked FICO to help with a pre- and post-conversion strategic plan. Executives wanted to ensure there would be no negative surprises preventing the bank from hitting its ROI target for this new asset.
FICO consultants performed a cross-lifecycle analysis of portfolio profit components and forecasted... [Read More]
Our latest infographic identifies the eight characteristics of a Big Data Analytics Software (BDAS) developer. BDAS developers write the code that sits in every analytic application, service and solution, powers the analytic engines of the future, and turns massive complex data streams into clear insights in real-time. The characteristics are: RADical programmer (rapid application development that is), teamster (good in the scrum), parallel processor (high performance computing and parallel processing), captain of chaos (love for NoSQL), polygot (codes in multiple languages), open sorcerer (Hadoop and Hive is their domain), go Git-er (finds it on GitHub and BitBucket), and fearless explorer. Check out the infographic to see if you have what it takes to be a BDAS developer.