BIG DATA IN THE FINANCIAL WORLD

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1 BIG DATA IN THE FINANCIAL WORLD Predictive and real-time analytics are essential to big data operation in the financial world Big Data Republic and Dell teamed up to survey financial organizations regarding their use of big data to drive competitive advantage. A total of 291 financial professionals gave us their feedback on how they are leveraging big data technologies to lower risk and retain customers. From the resulting data, a clear one of five organizations seems to be benefiting by taking advantage of advanced technologies, while the rest are fast falling behind. Leveraging benefits from data We started by asking these respondents how efficient their financial organizations are at leveraging benefits from data. This broad question was designed to set the scene for big data preparedness, inviting an answer that doesn t just relate to financial uses of data analysis, but analysis across the broader business (job metrics, marketing, sales, etc.). The responses were modest and revealing about overall competencies. 1» Big Data in the Financial World

2 Only one in ten of our respondents indicated being at peak performance when it comes to data utilization. While this seems like a low response, the question was couched in the environment of a big data discussion, indicating that views on this answer might be tempered by the individual s understandings of the newer, lofty ambitions of big data projects. Regardless of the relatively low count of respondents willing to boast about their data prowess, this can be taken as a sign that the overall respondents had mature and grounded mindsets and are well aware of the challenges to come. Combining the two worst responses ( we have a long way to go and we don t benefit from our data ) yields a return of 33% meaning one third of respondents are uncomfortable about leveraging the benefits big data solutions can offer in the financial space. The remaining 56%, who claim to be pleased with what they are doing so far but know they could do better, represent the core group who are positioned well to leverage new and improved financial tools. The peak performance group will clearly be in a great position to build on successful systems in place, but the boldness of their answer may indicate a lack of awareness in where improvement could be made. How efficient is your organization at leveraging benefits from data? We re at peak performance 10% We re pleased with what we re doing but know we could get better 56% We have a long way to go 31% We don t benefit from our data 3% Leveraging more revenue We then asked our respondents to change tack a little, to focus on their ideal scenarios for data management, asking: If you had to choose only one of the following benefits from your data, which would it be? They could choose among extracting more profit from current customers, finding new customers, or better customer retention. Forty percent of our respondents indicated their first choice would be on finding new customers. At first glance this statistic seems to veer the conversation away from big data solutions specific to the financial toolkit and make it a more holistic marketing conversation. However, the attraction of new customers to use financial products can be defined both through a marketing approach and from a customer value defining / attractive financial product offering viewpoint. Both of these two final points are more advanced data plays, which can combine efficiently with any marketing drives. The concept of a new customer also bleeds over into extracting more profit from current customers, an answer that garnered 32% of our respondents votes. The act of defining a customer not just by actual value to a financial product, but by potential value, allows room for innovative product tweaks and launches to extract more value from the customer. If this value can be determined at the point of finding new customers, the drive to bring these customers on board can be more efficient in terms of both time and money spent. Achieving better customer retention yielded the least interest, with only 28% choosing this option. Such a result could be an indication of poor company culture (relying on bringing in new customers despite significant cost in comparison to retaining customers), good existing customer services, or low attrition rates. Extracting more profit from current customers 32 Finding new customers 40 Better customer retention 28 2» Big Data in the Financial World

3 Reducing risk Having set the scene through these two opening questions, we started accessing the companies individual capabilities at using financial-specific data to provide pragmatic solutions. Focusing on exposure and risk, we asked How efficient is your current risk-assessment/credit-scoring system? The split between progress and under-development in this area is predictable, with almost the same number of people implying they had no further improvements to make or that there is no current capability in the area (19% and 18%). Meanwhile, organizations with a functioning (albeit overly simplistic) credit scoring system in place made up the largest group of respondents with 34%. Second place went to companies whose systems were in place but exposing them to too much risk. In all, the risk assessment question revealed an immature area of big data financial deployment, which has started to gain some real traction but is being overlooked or under-used by a large section of the business community. It s perfect; we have access to the customer base we need without exposing ourselves to risk 19% It s too simplistic; we need a more dynamic system that uses more data to identify potential customers currently falsely being identified as high risk It needs tightening up; we re exposing ourselves to too much risk 29% We aren t currently able to carry out useful risk-assessment/credit-scoring 18% 34% Switching from internal risk to external issues concerning fraud, we asked How efficiently can you identify fraudulent activity? Given that financial institutions stand to lose so much to fraudulent activities, it doesn t come as much of a surprise that the split between those who excel and those who have barely started is bigger, with five percentage points separating those with no continuing fraud problems at 14% and those desperate for help at 9%. Likewise, the maturity of organizations using data to reduce fraud threats is highlighted when we examine the 56% who claim to be vigilant with room for improvement, and those who are vigilant but can only tackle fraud once the incident has taken place. This 12% who are stuck in a retroactive response environment are essentially powerless to stop fraudulent activity occurring, and only able to deal with the aftermath. This fraudulent activity pattern spotting is a huge benefit brought to the financial world through the processing power of big data technologies and so, when combined with the 9% who desperately need help adopting detection systems, creates 30% of respondents missing out on a vital financial tool set. We excel in this area; fraud problems are a thing of the past 14% We re pretty vigilant but could do with improvement 56% We re pretty vigilant but can only track fraudulent activity retroactively 21% This is an area we need help in 9% That still leaves 70% who are in a good to excellent position to tackle fraudulent activity as it is occurring, which is a reassuringly large slice of the pie. To further examine reasons for this strong position, we compared answers from the question How efficiently can you identify fraudulent activity? with data from Figure 1, How efficient is your organization at leveraging benefits from data? Looking at those who, regarding their overall data, claimed to know what they are doing but also feel they could get better it seems their concerns lie away from the world of fraud. Examining those who overall felt they know what they were doing but need improvement, only 14% lack proactive fraudulent tools, and only 2% desperately need help 3» Big Data in the Financial World

4 with fraud detection. This is in comparison to a respective 14% and 3% among those who claimed their data usage is running at peak performance. This indicates that either proactive fraud detection is easy to implement (unlikely) or that these businesses have been prioritizing this area and harvesting best-practices to get the results they are after. Even among those who claim to have a long way to go when it comes to their overall data positions, proactive fraud detection (combining those who excel and those who are vigilant with room for improvement) makes up 44% of responses. It s fair to state that financial organizations are showing great vigilance when it comes to fraud detection meaning those who aren t investing and developing in this area will have a sharp learning curve to deal with if they aren t to lose some serious ground on competing services. We excel in this area; fraud problems are a thing of the past We re pretty vigilant but could do with improvement We re pretty vigilant but can only track fraudulent activity retroactively We re at peak performance We re pleased with what we re doing but know we could get better We have a long way to go 62% 10% 5% 21% 74% 39% 14% 14% 38% This is an area we need help in 3% 2% 18% Customer satisfaction If safety is one core part of a financial offering, attractiveness is definitely the other, and so we switched attention to how data is being used to keep customers with the business. We asked how proactive organizations are in refining product offerings, hoping to learn how attuned financial organizations are to the way their product is received. Reassuringly, only 7% claimed to have no insight or ability to change their offering in response to customer behavior. Such a position makes it nearly impossible to compete with other financial institutions, so we can only assume these organizations provide a very specific service with no direct competition or perceived need to evolve, or they are stagnating badly. Twenty-seven percent have the ability to look at product offerings and tweak, but they only do this when significant downturn is noticed. A larger 44% chunk analyze data to refine product offerings, but only do so a few times a year. Combined, this is 71% of financial institutions using data or big data to define product offerings, but in a very reactive manner meaning staying ahead of competition (or determined customers) is difficult to manage. That leaves 22% who, along with utilizing the size of data available to them, are taking advantage of the speed at which they can do so, so that they work proactively to maintain engagement with customers. This can be a mix of monitoring customer interaction/behavior when they are interacting with any online manifestation of the financial product, or acting on insight borne out of customer surveys, etc. How proactive are you in refining product offerings? We sift through data regularly to tweak our offerings 22% We analyze data on this and make adjustments a few times throughout the year 44% We can do this, but we only do it when we know things are going wrong 27% We don t have the ability to analyze this 7% 4» Big Data in the Financial World

5 Creating attractive options for the customer in this manner goes a long way to negating the next step in the process, which is intervening when it becomes clear that a customer is planning to leave. It has long been established that retaining customers is cheaper than finding new customers, and so the 22% who find themselves in a proactive space should benefit significantly in the long term. Of course, there are times when a customer may leave anyway, through indifference to your product or a variety of other reasons. This area of customer retention is where we focused our next question: How good are you at identifying customers looking to leave? How good are you at identifying customers looking to leave? We usually know about it before the customer does 22% We re getting there, but some slip through the net 67% The first we know is when they ask us to close their accounts 11% We focused the question around awareness, rather than the steps taken to prevent departure as methodologies for doing so will vary wildly from financial product to financial product. There is a reassuring level of maturity in this space, which requires the data to work as an intuitive mind, spotting patterns of discontent that lead up to attrition. For instance, only 11% generally lose customers without seeing some signs of it coming. If we think back 10 to 15 years, the process of understanding customer desires and drives was a very time-intensive, hands-on process. Now we see just over one in ten respondents who aren t capable of picking up on the signs leading up to a customer s departure. This indicates a broad understanding of what big data technologies can do to flag up customer retention, coupled with financial services seeing great benefit in equipping themselves in this area. However, looking one level above, we can see 67% of our respondents have started to employ customer attrition recognition tools, with limited or incomplete success. Taking the lion s share of responses, this option belies exactly how advanced we have become in this arena. Although this is obviously an improvement on no vision, it indicates a long path ahead before real benefit is seen. Despite this, the 67% result does indicate a positive figure for awareness of these technologies in the financial space. Confidence in this space is held by 22% of respondents who told us that they usually know that the customer is planning to leave before the customers themselves do. That means around 1 in 5 respondents are ahead of the curve in this space, using the most advanced form of predictive analytics and pattern recognition to the benefit of business. This trend seems to mark out a fifth of businesses operating in this space taking full advantage of the data available 22% who tweak offerings to maintain product attractiveness and 22% using predictive analytics to determine when customers might be able to leave. This elite group is building an analytic advantage that will be difficult for latecomers to the space to overcome. The following graph explores the relationship between the ability to identify dissatisfied customers and overall organizational data capabilities. The difficulty level of achieving this insight is highlighted clearly if we ignore results from those who already feel they are running at peak performance. Those who are pleased with their overall data strategy mimic the success rates of the overall group but with a higher percentage (73%) who are seeing early-level benefit and a lower number (6%) who have no insight at all. Note, interestingly, that this last percentage is actually marginally smaller than the equivalent percentage found among those claiming to be operating at peak performance when it comes to data. 5» Big Data in the Financial World

6 How good are you at identifying customers looking to leave vs. How efficient is your organization at leveraging benefits from data? We usually know about it before the customer does We re getting there, but some slip through the net The first we know is when they ask us to close their accounts We re at peak performance We re pleased with what we re doing but know we could get better We have a long way to go 59% 21% 12% 34% 73% 69% 7% 6% 19% This leaves us to focus on the laggards who have a long way to go. This group highlights the difficulty of identifying customers on the brink of attrition if the overall data strategy is not in place. A huge 88% in this group have either limited or no insight into customer attrition patterns. This, in both long and short terms, places them at a significant competitive disadvantage Difficulty vs. success Bearing this level of difficulty in mind, let s examine a few of these key data insight responses together: How efficiently can you identify fraudulent activity? We excel in this area; fraud problems are a thing of the past 14% We re pretty vigilant but could do with improvement 56% We re pretty vigilant but can only track fraudulent activity retroactively 21% This is an area we need help in 9% How good are you at identifying customers looking to leave? We usually know about it before the customer does We re getting there, but some slip through the net The first we know is when they ask us to close their accounts 22% 67% 11% How efficient is your current risk-assessment/credit-scoring system? It s perfect; we have access to the customer base we need without exposing ourselves to risk 19% It s too simplistic; we need a more dynamic system that uses more data to identify potential customers currently falsely being identified as high risk 34% It needs tightening up; we re exposing ourselves to too much risk 29% We aren t currently able to carry out useful risk-assessment/credit-scoring 18% Despite the level of difficulty established when it comes to being able to identify customers willing to leave, we can see that this is an area more companies are excelling at (22% vs. 19% in risk assessment and 14% in identifying fraud). Possible explanations are the overall reward for focusing on this area (the argument around customer retention and customer attraction) or the way that data is leveraged in different departments internally. While utilizing data to retain customers (or at least spot that they are planning to leave) revolves around close to realtime analysis, the patterns of behavior customers display on their way to exiting are at least predictable to an extent. This might explain the relatively low scores respondents are displaying in response to their risk assessment and fraud detection projects. While these also rely on getting insight in close to real-time, the patterns and variables involved can be much more difficult to understand. This is especially so when considering organized fraud, which will actively seek to break pattern detection systems. 6» Big Data in the Financial World

7 Looking forward for financial organizations In conclusion to our survey, we asked respondents to let us know what is top of mind when it comes to their choice of IT and big data solutions which spread more light over the progression documented above. When asked to indicate what is most important to them, 32% indicated budgetary concerns would have an impact on implementations. Second on this list, and heartening when it comes to business progression, almost a quarter (24%) indicated performance, without regard for cost, was the most important element for them, which would go some way to defining that elite 22% when it comes to using predictive analytics. The rest of this list was made up of price as a main concern (12%) and controlling the space as an area of focus (3%). A further 29% couldn t pick a more important choice among those listed, indicating the complex nature of operating within a financial organization. The most important thing to me is: Budgets are tight; I need to control my expenses 32% Performance is everything; I ll deal with price later 24% Real estate, real estate, real estate; I need to control the space 3% Price is at the top of the list 12% I suppose all of the above are important to me 29% We then turned attention to plans to improve datacenter efficiency which would obviously have a bearing on all capabilities explored in this paper. From the responses below it is clear that the datacenter is still struggling for attention among the other business elements, with 44% of respondents indicating it will happen, but not immediately, and a further 25% pushing it further down the list of priorities. Plans for improving datacenter efficiency are: Top of mind; I intend to make changes in the next three months 16% Kind of important; we will need to make changes in the next couple of quarters 44% Lower on the list; will move to the late 2013 or 2014 to-do list 24% I m not too concerned with datacenter efficiency since facilities has a different cost center 16% Conclusion Financial organizations are well on the way to realizing the benefits that aligned predictive data projects can bring to the way they serve their customers. In this vertical, the correlation between a successful project and lost (through fraud) or increased (through customer retention) revenue is abundantly clear. More so than many other industry sectors, real, practicable insight here will depend on real or close to real-time analytics. A solid fifth of organizations are excelling in this area, but those lagging behind will find it difficult to come back from such a compromised competitive position. 7» Big Data in the Financial World