January 19, 2016 by Leave a Comment
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 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.
July 21, 2015 by Leave a Comment
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.
October 15, 2014 by Leave a Comment
Data analytics is not a new pursuit. SAS, for example, has been offering solutions since its inception in 1976. But owing to the inherent complexity of advanced data analytics platforms, experience with data analytics has been the domain of only the largest organizations. However, the last several years have witnessed an explosion in applications for data analytics, especially in the area of customer analytics. With the growth in applications came conveniently pre-configured software solutions that were fine-tuned for a bevy of specific applications. The combination of product evolution, specialized analytics-savvy consultants, professional services firms, and cloud computing, has brought advanced analytics swiftly down market. Now, even small banks and credit unions can foray into customer analytics with a comparatively small investment and without a legion of data scientists on staff. But are they? Well, that depends on what you mean by data analytics. Celent recently surveyed about 100 North American banks and credit unions to understand the state of analytics adoption and the drivers behind its growth. In our resulting report, “Customer Analytics Adoption in Banking: When Management Doesn’t Lead” (September 2014), we noted that about half of the financial institutions in the sample had some experience with data analytics. However most of these efforts might be considered rudimentary, such as customer profitability or web analytics applications. A third of the respondents to the survey had experience with social media sentiment monitoring, an example of advanced analytics, but inexpensively available in the cloud and easily used by non-data scientists. In contrast, usage of predictive analytics applications is far less common. Just one in five financial institutions demonstrated experience with next-best-action analytics, and one in ten showed an understanding of customer lifetime value. What gives? If customer analytics holds such great promise, why aren’t more banks and credit unions deriving value from its use? I think there are at least two reasons. First, we are seeing an immature state of data analytics at most financial institutions. Second, and perhaps more important, there appears to be a lack of interest by leadership at the top of these financial institutions in driving data-driven strategies. Is Your Organization Data-Driven? Using data to make decisions is not the same as being data-driven. An organization doesn’t become “data-driven” simply by installing an advanced data analytics application. So, what does it really mean to be a data-driven organization? Celent asserts that data-driven organizations use analytics extensively and systematically to influence and execute strategy. Practically, this takes many forms, but it begins with attitude. Organizations start by deciding to value data, develop confidence in its validity, and make decisions based upon data even when doing so is uncomfortable. In other words, being data-driven amounts to having faith in the efficacy of data and acting accordingly. It means walking the walk, not just talking the talk. How many banks are true data-driven organizations? Not many, we find. It’s probably fair to say that the concept of an organization being data-driven isn’t a binary thing. Instead of a “yes” or “no” answer, perhaps the question is best posed, “How data-driven is your institution?” and additionally; “How data-driven would your organization be if it were up to you?” The survey found that just 29% of responding financial institutions thought their organizations were highly data-driven. Nearly 90% of that same sample said their organizations would be highly data-driven if it were up to them. In other words, they wished for it. Clearly, we think that the industry wants to be data-driven, but doesn’t think it’s there yet. Source: Celent survey of North American financial institutions, July 2014, n=78 Lack of Leadership Intuitively, this suggests a leadership problem, but does the data support this conclusion? It does. We cross-tabbed the survey results by respondent roles and found significant differences in attitudes surrounding data analytics. Specifically, responses to the question “How data-driven would your organization be if it were up to you?” varied dramatically by role. It turns out that all respondents in IT/IS roles wished their organizations were highly data-driven – or would be if it were up to them. In contrast, respondents in strategy or innovation roles as well as those in marketing, showed somewhat less passion for being a data-driven organization. Perhaps a surprise, coming in last in support of data driven strategies were those in executive management; compared to those they lead, this group was the least desiring for their organizations to be data-driven. Source: Celent survey of North American financial institutions, July 2014, n=78 Although surveys aren’t the final word on any topic, the results do suggest a leadership problem, which if addressed, would go a long way towards better serving customers through skilful use of data analytics. As banks better understand the merits of being data-driven, we think that financial institution leadership will ultimately lead the march to supporting data-driven business strategies, particularly those focused on customer analytics. I will be addressing this topic in more depth in a session at American Bankers Banking Analytics Symposium in New Orleans on Thursday, October 16th.