Celent Model Bank Awards: Fraud, Risk Management, Process Automation and Flub-Free

Celent Model Bank Awards: Fraud, Risk Management, Process Automation and Flub-Free

It is my privilege to be part of the judging panel for Celent Model Bank Awards for 2017 for the following three categories:

  • Fraud Management and Cybersecurity – for the most creative and effective approach to fraud management or cybersecurity.
  • Risk Management – for the most impressive initiative to improve enterprise risk management.
  • Process Automation – for the most effective deployment of technology to automate business processes or decision-making.

A common theme across this year’s submissions for the above categories is the importance of agile technology, digital process automation, and consistent and focused practices across the organizations. A large number of the entries show that a streamlined and automated operational risk framework is critical to run a successful risk management program. Everything connects and has a consequence and unless banks can join the risk dots across their ecosystems, they will continue to spend at a very high rate with unsatisfactory and, at times, devastating results.

Improved data analysis and machine learning capabilities also featured prominently in the winning case studies. A central data platform, automated processes and improved insights have produced notable increases in efficiency, better control of costs, reduced resourcing requirements, reduced errors and false positives and have made it easier for the banks to adapt to their digital footprint, an expanding cyber threat landscape, and intense and complex regulatory obligations.

Hopefully, no flubs on the big day

Without exception, every submission is of a high-quality and we found it a daunting task to pick the most worthy award recipients. In the end, we are excited and confident about our selection of winners in the above categories, yet we are sorry that we could not recognize so many others that clearly also deserve recognition.

At the moment we are staying tight-lipped about who won the awards. We will be announcing all winners publicly on April 4 at our 2017 Innovation & Insight Day in Boston. In addition to presenting the award trophies to the winners, Celent analysts will be discussing broader trends we’ve seen across all nominations and will share our perspectives why we chose those particular initiatives as winners. Make sure you reserve your slot here while there are still spaces available!

 

Large FIs spent $25M rolling out failed risk management frameworks during the 2000’s. So why try again?

Large FIs spent $25M rolling out failed risk management frameworks during the 2000’s. So why try again?
Large financial institutions spent in excess of $25 million on rolling out failed enterprise risk management frameworks during the 2000’s. So why try again? Well for many obvious reasons, the most notable of which has been the large scale failure of institutions to manage their risks and the well-editorialized consequences of those failures. The scale of fines for misconduct across financial services is staggering and damage to the banking industry’s reputation will be long-lasting. Major Control Failures in Financial Services blog Source: publicly available data Regulators and supervisors are determined to stop and reverse these risk failures, specifically, the poor behavior of many bankers. Regulators are demanding that the Board and executive management take full accountability for securing their institutions. And there is no room for failure. This is the only way that risks can be understood and, hence, managed across the enterprise. There is no denying that risk management frameworks are hard to implement but Celent believes the timing is right for the industry to not only secure their institutions and businesses but to innovate more safely and, slowly, win back the trust of their customers. My recently published report Governing Risk: A Top-Down Approach to Achieving Integrated Risk Management, offers a risk management taxonomy and governance framework that enables financial institution to address the myriad of risks it faces in a prioritized, structured and holistic way. It shows how strong governance by the Board is the foundation for a framework that delivers cohesive guidance, policies, procedures, and controls functions that align your firm’s risk appetite to returns and capital allocation decisions.

Don’t be surprised if your bank knows not just who but also what you are in the future

Don’t be surprised if your bank knows not just who but also what you are in the future
We all know personality tests can be a little hit and miss – some are serious, long and can be scarily accurate. Others you do for fun on a Saturday afternoon whilst reading a magazine, and you never take the results too seriously. I just came across a new type of personality test, Personality Insights powered by IBM’s Watson. According to the description, the test “uses linguistic analytics to extract a spectrum of cognitive and social characteristics from the text data that a person generates through blogs, tweets, forum posts, and more.” Interestingly, it claims to be able to reach conclusions just from a text of 100 words. I was curious to see what the tool would say about me based on some of my blogs. I entered one of the recent texts and I got this back:
You are inner-directed and skeptical. You are empathetic: you feel what others feel and are compassionate towards them. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are independent: you have a strong desire to have time to yourself. You are motivated to seek out experiences that provide a strong feeling of connectedness. You are relatively unconcerned with taking pleasure in life: you prefer activities with a purpose greater than just personal enjoyment. You consider achieving success to guide a large part of what you do: you seek out opportunities to improve yourself and demonstrate that you are a capable person.
As always with these things, you never entirely agree, but I could recognise some of my personality there, so I was intrigued. I wanted to try it more and started entering other blogs written by me and my colleagues on this site. Most of the results turned out to be remarkably similar, suggesting that we are “shrewd, skeptical, imaginative, philosophical, driven by a desire for prestige, relatively unconcerned with tradition, etc.” Well, it is possible that we are a fairly homogeneous bunch – as analysts we often talk about new technologies, so we are “relatively unconcerned with tradition”, yet we can’t afford to succumb to the latest hype, so can come across as “skeptical.” But the homogeneity of results made me rather suspicious, so “for something completely different”, I entered an article on English football by a broadsheet journalist. While his profile turned out to be a bit more different, he was also “inner-directed, skeptical, empathetic, and philosophical.” Not surprisingly, I wasn’t the first person to try out the tool with the extremes. A Mashable article described someone submitting “a 1919 letter from Hitler explaining his anti-Semitic agenda to a well-wisher” for analysis. Apparently, Hitler was also “shrewd, skeptical, imaginative, philosophical, laid back, appreciating a relaxed pace in life” and someone who thinks “it is important to take care of people around you.” Now, it’s easy to show how something new is not yet perfect, but there is serious science behind the service. And even though this particular tool still needs to learn and improve, we are convinced that artificial intelligence and Watson-type technologies will have a big impact on customer servicing in Banking and other industries. Implementing and making use of these technologies is not easy, but there is no doubt that in the future more decisions will be driven by data and analytics. So, don’t be surprised if the next time you call up your bank to discuss the latest transactions or the new product you want to buy, you realise they know instantly not just who you are (e.g. via voice biometrics), but also what you are. P.S. I just did sort of a “meta-test” by entering the above text into the service. The tool called me “unconventional” and suggested that I am “intermittent” and “have a hard time sticking with difficult tasks for a long period of time.” Is it not just smart, but potentially vindictive as well? 🙂

IBM’s Cognitive Bank: Big Data, bigger problems

IBM’s Cognitive Bank: Big Data, bigger problems
Last Wednesday I attended IBM’s analyst presentation on Transforming Banking and Financial Markets with Data. The crux of the presentation was the benefits of big data and cognitive analytics for financial markets. The return from better understanding the desires of an individual bank customer are well understood and IBM did a good job of illustrating the up-lift. But what were not discussed are the daunting challenges and complexities a bank will face in implementing and managing a big data project. The implementation and ongoing management of data will make or break the success of cognitive computing. What I would like to see is an open discussion on the successes and failures of big data implementation programs by the banks, IBM, and other vendors working in this space. How smooth was the implementation process (time/budget/resourcing etc.)? Were your expectations set correctly? Did you get the required support from management? What were the lessons learnt? What value do you see from your big data program? It’s not easy Structured data tends to sit in multiple databases housed in silo-ed legacy systems; it is customized, lacks consistency, has incomplete fields, is often latent in nature and is prone to human error. All of which compounds the complexity of managing the data. Add to structured data the volume, variety, and velocity (known as the 3 Vs) of unstructured data and the challenge of implementing and managing information becomes even greater. And, the larger and more complex the bank the more likely its data architecture and governance process will hinder data-based implementations projects. Automating the management of data is time consuming and laborious and scope creep is significant, adding months onto implementation projects as well as extra expense and frustration. Resourcing such projects can be taxing as there is a limited pool of big data expertise and they are expensive. To perform cognitive analytics, massive parallel processing power is required and the most cost-effective operating environment is through the cloud. If you get the data right, cognitive analytics can be very powerful. Cognitive analytics Cognitive analytics (also referred to as cognitive computing) is a super-charged power tool that allows data scientists to crunch vast amounts of structured and unstructured data and to codify instincts and learnings found in that data in order to develop hypotheses and recommendations. Recommendations are ranked based on the confidence the computer has in the accuracy of the answer. How you rate confidence was not made clear by IBM and I would argue that this can only come after the fact, when you can use KPIs to validate the scoring and criteria. The modeling techniques include artificial intelligence, machine learning and natural language processing and, unlike us mere mortals, the more data you feed the computer, the higher the quality of the insight. If you do get it right, the rewards are significant We continue to leave behind mind-boggling amounts of digital information about our lifestyles, personalities, and desires. A sample of sites where I know I have left a hefty footprint include Facebook, Reddit, LinkedIn, Twitter, YouTube, iTunes, blogs, career sites, industry associations, search history patterns, buying patterns, geo locations, and content libraries. IBM Watson offers banks a cost-effective way, through the cloud, of scouring such data to build up clues that provide a more in-depth view of what their customers’ desire. Current analytic segmentation is requirements-based and is modeled on past behavior to determine and influence future behavior. The segmentation buckets are broad and all within them are treated the same. Cognitive analytics allow a much more precise and immediate analysis of behavioral characteristics in different environments and, therefore, a more personalized and satisfying experience for the customer. I’d welcome any feedback from those of you who have been involved in implementing or are in the process of implementing big data in banking. And, if you’re interested, take a look at Celent’s Dan Latimore’s blog Implementing Watson is Hard On a side note, IBM introduced the term Cognitive Bank and it is not a phrase that works for me. It is disconcerting to describe a bank as having the mental process of perception, memory, judgment, and reasoning. Looking forward to hearing from you.  

Customer Analytics: Time to get Your Feet Wet

Customer Analytics: Time to get Your Feet Wet
I had the pleasure of speaking at Fiserv Forum 2014 in Las Vegas last week, discussing “The Payoff of Turning Data into Action.” During the presentation, I offered some suggestions to financial institutions that have not yet made inroads into customer analytics. Why Here, Why Now? Quite a few community bankers have resisted implementing CRM solutions, for example, and lived to tell about it. Like big data, the promise of CRM in its early days was somewhat overblown. But, that was then. Is customer analytics the New CRM? I say “no” for at least four reasons: 1. The “new normal” in retail banking – Banks need to grow top-line revenue, but it is increasingly hard to do. Analytics applied to customer segmentation, marketing and customer experience can play a critical role. 2. The growing imperative for customer centricity – As consumers increasingly interact digitally with financial institutions, the branch channel is losing relevance and impact. In addition to improving branch channel efficiency and effectiveness, banks must learn how to engage customers digitally. Analytics is the way to do so. 3. Technological advancements – Analytics used to be the domain of data analysts and large, expensive implementations, but modern analytics applications are tailored for business users and integrated with business applications. Getting started is no longer expensive. 4. There’s money to be made – As the use cases for customer analytics multiply faster than rabbits, financial institutions are finding a growing number of ways to profit from customer analytics. In a 2013 survey of North American financial services firms, 70 percent of those having at least one year’s experience with one or more big data initiatives met or exceeded their business case. Not a bad batting average, to be sure. If you remain unconvinced, the Celent report, Customer Analytics in Retail Banking: Why Here? Why Now? may persuade. Getting Your Feet Wet How does an organization get its feet wet with customer analytics? Are there best practices for turning data into action? From interviews with a number of those in the 70 percent, as well as banks who struggled initially, I offer these getting started tips. • Begin with the end in mind – Analytics is a means to an end. Successful examples of data analytics share a common element of focused energy to achieve a limited and specific business objective. • Start small, remain focused – Like its sister topic, big data, there is really no end to customer analytics. Unlike CRM projects, one is never through with analytics – its very nature requires continual refreshing of models and their use. Analytics invites a new way of doing things as much as it invites using new technology. Get started with a single, manageable project and prove its value before moving on. • Get help – There is a steep learning curve associated with fully leveraging data analytics. A modest up-front investment in assistance from firms that specialize in analytics may hasten your project deployment and product better results. Fiserv is well positioned to help – and may already be hosting your data. • Change your culture – Benefitting from analytics requires a devotion to cultural, organization and procedural change. That’s why it is important to start small. Cultural change can and will come alongside socializing the value of early successes. Tom Davenport has authored several books that shed light on the power of making analytics more than an IT project: Analytics At Work, and Competing on Analytics. • Manage expectations – Firms like Amazon and Google make analytics look easy. It’s not. Deriving benefit from customer analytics will be more of a journey than a destination and the road will seem long at times. All the more reason to get your feet wet soon.

The Computer Trading Debacle and Data Analytics Lessons for Banks

The Computer Trading Debacle and Data Analytics Lessons for Banks
Last Tuesday, 23 April, a hacked Twitter message from the Associated Press about an attack on the White House that injured President Barack Obama, turned out to be bogus. But the tweet sparked a brief 145 point market selloff that dramatized the power of algorithmic trading – sophisticated computer software that analyzes language using algorithms to allow high-speed trading in financial markets. More on the event is available here. I’m not qualified to comment on algorithmic trading (more than I already have…), but do see a teachable moment in this event for banks exploring the use of data analytics for less controversial applications such as marketing and risk management. Two points come to mind.
  • Algorithms are stupid. For all their elegance, algorithms have no wisdom. This is not to say that data analytics is not highly useful (it is!), but the underlying algorithms are powerless to perceive their impact. That step requires perceptive humans willing to undergo some rigor and discipline.
  • Models are fiction. Data analytics is based on the construction of simplified models of things that have relevant business interest. Their simplicity is important, because it allows modelers to isolate variables that are relevant and available. But, it is all fiction – an approximation of reality. For this reason, models must be validated and results treated with care. Next best product prompts will be wrong some of the time. Virtual agent prompts will be nonsense some of the time.
These two aspects create both challenge and opportunity for banks seeking to leverage data analytics for competitive advantage. Culturally, doing so requires a deep commitment to a “test and learn” way of doing things. One is never finished in this new world. There is always opportunity for improvement. Models get stale and must be continually revisited. Celent observes a useful data analytics process in place in a number of banks (below). Data Analytics Process It’s hard work. That may be one reason why so few banks are broadly deploying these new technologies. But, it’s rigor that can’t be avoided if you want the good without the bad.