Unlocking the value of transaction data New source of value for payments?

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1 Unlocking the value of transaction data New source of value for payments?

2 Contents 1. Transaction data as a source of value 2. A shift in focus: Data as a service for clients 3. Added value of Data as a service 4. Unlocking value means locking into commitment 5. Road to success 1

3 Executive summary Capture the value of transaction data: act now! Client needs, new competitors, regulation, and pressure on revenue force banks to shift their focus to innovation. Insights from transaction data provide an opportunity for value creation. Nowadays, data driven value creation is primarily focused on the bank s internal cost reduction and revenue optimization. Next step is to explore opportunities to optimize the client s business: Data as a service. There are large indirect benefits of data as a service for banks: strengthening the role as trusted financial advisor, more ease and relevance in client interactions, and opportunity for differentiating from competitors. New business models and pricing mechanisms need to be explored to capture the direct value of these services. Unlocking the added value of transaction data requires addressing four key success factors: building executive commitment, a lean approach focused on business results, centre(s) of excellence, and safeguarding customer trust & privacy by design. The road to success requires a structured approach, driven by the strategic priorities. Competition on Data as a service propositions is emerging quickly, so banks must act now to seize this opportunity themselves. 1

4 1.Transaction data as a source of value We believe that there is a large opportunity and necessity for banks to create more added value for their clients and explore new revenue sources by transforming data in to new services. By nature, banks sit on a huge amount of data, with a pivotal role for transaction data. This paper focusses on how Payments can unlock this value. Call for added value and new revenue source For quite a number of years now, banks have been focusing on streamlining Payment operations, reducing legacy complexity, and complying with new regulation. Nowadays, there are fundamental challenges that require a shift from an Operational Excellence towards a customer and value driven agenda. Pressure on fees: for years payment revenue is under stress, as shown by declining interchange fees, low interest rates, and downward pressure on transaction fees, particularly in the corporate market. The overall pool of cash management revenue for banks will further decline by more efficient corporate cash management and less banks per corporate in the new SEPA world. New sources of revenues will need to be explored, most urgently in mature sophisticated markets such as those in most of Europe. This urges the need for innovation. Regulatory interventions: politicians and regulators will continue to encourage product innovation based on a more transparent and open level playing field. The discussion around third party access to the bank account (the Payments AG case) illustrates this point. Banks will probably have no longer the monopoly on channels and access to account data. Regulatory interventions will accelerate new business models and propositions focusing on ease and added value in transaction channels for corporates and retail customers. In response, banks will need to innovate their channel offerings. Cross-industry competition: the value of transaction data has been recognized by others. Competition will increase further with the risk that new entrants often from other sectors such as Information & Telco industries are bypassing parts of the traditional banking system with new solutions. Not only by offering easy transaction channels, but by owning the digital customer experience. Examples include Amazon, Google, Square, and Mint Matured technology: nowadays, limits in storage, processing and analyzing of data have been reduced significantly in terms of computing power and software boundaries. This enables a much broader use of the already huge amount of internal and external data available to banks, with transaction data in a pivotal role. Most pressing challenge for banks is therefore to address the clients' call for providing real added value and combine that with new revenue sources. Payments holds a key to both, if the bank is able to unlock the value of transaction data. We ve just started the journey Transaction data has been available for a long time, but banks have yet to unleash the full potential. This is due to: Internal focus on regulatory compliance and on cost and complexity reduction; Limited belief and unfamiliarity with the business model regarding how to successfully market new data driven service propositions into revenue generators; Organizational challenges, capabilities and expertise are spread over business lines and too often too much focus on technology; Cost usually associated with data programs: as seductive as so-called (big) data may seem, the associated costs are a burden on the business case and the quest for a lean and customer focused application has just started; Real and perceived complications with privacy regulations in using available data commercially. First concepts of using available transaction data commercially (whether or not combined with other enterprise and external data) are put into practice by banks to optimize the sales of their existing products. Offering transaction data analysis to retail corporate and merchant clients as a value added service ( Data as a service ) to improve their business has only just started. This is where banks can show their distinctive capacity to clients, enrich the customer experience and simultaneously tap into new sources of revenue. 2

5 1.Transaction data as a source of value What is the road to success? In order to enable Payments to unlock the value some crucial questions need to be answered: a) What are the strategies to realize value from transaction data? b) What is the added value of the Data as a service propositions? c) What are the main critical success factors? More specifically: what are the opportunities and boundaries in leveraging (transaction or client) data within a trusted relationship with clients? d) What should banks do to fully exploit these new opportunities? 3

6 2. A shift in focus: Data as a service for clients We see four generic strategies for data driven value creation. Currently banks focus on the internal datadriven value creation strategies where we believe that the focus should be more on data as a service propositions for the bank s clients to capture the substantial value that can be gained. Substantial value to be gained In search of new ways of revenue growth, cost reduction, and attracting capital, the financial services industry can capitalize on transaction data in a substantial way compared to other industries. The potential value is high based on the amount of data that is already available to financial institutions, with the transaction data at its core. This transaction data provides real-time insight in actual customer behavior. Realization of this potential value largely depends on the availability of the right capabilities, both human resources and IT infrastructure. Both tend to be relatively mature in the Financial Services Industry. Four strategies to capture the added value of data Investigating possible propositions for value creation asks for the distinction between two key factors; who is the primary beneficiary of the created value (bank vs. client) and what is the goal of the created value (cost reduction vs. revenue optimization). Figure 1 states the four categories in value creation: The lower half represents the internal value creation: data analysis is used to improve the business between banks and their clients, either with focus on the bank s cost reduction (quadrant 1) or on the bank s revenue optimization (quadrant 2); The upper half encompasses the external value creation: data analysis as an external service offering ( Data as a service ), to improve the business of the bank s clients. The services offerings aim either at cost reduction from the client s perspective (quadrant 3) or at the client s revenue optimization (quadrant 4). In materializing the potential value the real challenge is making the shift from an internal perspective on transaction data propositions to an external commercial one. Privacy to be considered: not a legal show stopper but pivotal in the bank s position as trusted advisor The commonly heard hesitation in using transaction data for commercial purposes is violating the client s trust in the bank as well as the legal and fiduciary responsibility of the bank. This sets the boundaries for the ways in which transaction data can be used. Within these boundaries however, examples have shown that client s trust and the bank s responsibility can be ensured. It all begins with realizing what kind of concerns may arise when engaging in data analytics with personal data. Once it is known that these are the situations to be avoided, a pragmatic approach can be applied to make sure they are. 4

7 2. A shift in focus: Data as a service for clients Figure 1. Strategies to unlock the value of transaction data and example service offerings Internal value creation: current focus of the bank To identify new sources of value the huge amount of data banks sit on should be explored. In addition, growing amounts of relevant external data are available. Currently this data is mainly used to optimize internal processes to reduce costs or enhance revenue and marketing effectiveness. Category 1: Bank s cost reduction Cost reduction (category 1) examples include risk reduction (e.g. early default warnings, Know-Your- Client), fraud detection (e.g. anti-money laundering), and analysis based on payments schemes and tokens to underpin its own investment decisions. A few examples: Examples Standard Chartered offers cheaper loans to clients who have multiple accounts with the bank, largely because it thinks the extra information allows it to assess risk more accurately. Large banks clean their data sources to improve their business, by tracking inactive customers or identifying customers as being one and the same person. Some propositions are still at their infancies, i.e. process mining for data driven process improvement and VAT reclaims based on transaction mining Category 2: Bank s revenue optimization Revenue optimization typically takes place by means of direct marketing of banking products to existing clients. Most large retail institutions have deep capabilities in the area. Many banks offer their customers a next best offer (NBO) based on available information. Research nevertheless shows that NBOs are often done poorly. One retail bank even stated that its NBOs were more likely to create ill will than to increase sales (Harvard Business Review, December 2011, Th. Davenport et al). A clear opportunity to augment the internal value creation, as it shows. Increasingly, data insights are also used to improve service and the customer experience. As online oriented clients are likely to become engaged in an online conversation with their bank, the conversation can be mined and analyzed in conjunction with other client data. The resulting insights can be used to inform and improve the quality of service. This social banking can transform the engagement between clients and banks from fire-and-forget financial transactions to even co-creation based on client s feedback on whatever forum it is provided. 5

8 2. A shift in focus: Data as a service for clients Some examples of propositions that can change customer experience: Examples Kiboo is an online banking service aimed at a younger demographic and seeks not only to provide a savings account but to educate savers on good financial management and help them to learn about how their money works for them. Smartypig allows savers to set public goals or savings targets and helps them to reach these targets by combining cash back savings and rewards with an interest rate and an element of gamification using social networking sites. It is designed to shift consumer behavior to a save-then-spend mentality. A large bank considers tweaking its systems to tell clients not just how much money is in their accounts when they ask for a balance, but also how much they will have available once all their usual bills are paid. External value creation: use of Data as a service to improve the client s business Combining transaction data with additional insights such as social media data, location data, and realtime point-of-sale feeds can also provide valuable insights for other companies to either reduces their costs or augment their revenues. Let s first focus on propositions that reduce costs for clients. Category 3: Data as a service to reduce clients costs Clients can benefit from (transaction) data insights provided by banks to reduce their own costs. Example propositions include: A direct debit scan to reduce default in payments, based on the bank re-using the already available internal risk and fraud analyses (e.g. KYC, credit risk); Support on financial supply chain management by means of cash management tools (e.g. reverse factoring, economies of scale), based on all the cash management data at the banks disposition; Forecasting and budgeting tools to support investment decision making, based on cash flow and spend analysis; Business optimization, for example advise on opening hours or shop locations based on the bank s trade analysis. Sharing experience and advice in using data for cost reduction increases the value for the client even more and intensifies the business relationship between bank and client. Example ID Analytics uses ID network, personal typology, and advanced analytics capabilities to build a picture of consumer behavior so that unusual or suspicious behavior can be quickly and easily identified. Any potentially fraudulent transactions can be stopped before they are completed if it is identified as such by the consumer. Category 4: Data as a service to optimize clients revenues Inventing ways for clients to use transaction data insights for optimization of their revenue will require out-of-the-box thinking. To provide a meaningful data analysis, cross-company and / or industry analysis will deliver just the insights that really provide added value for clients. Examples where the client can use the bank s transaction data analysis for revenue optimization include: Depersonalized statistics of markets and competitors to improve business results (e.g. average transaction value vs. average of competitors) Personal marketing based on social, mobile, and location information (e.g. timely for certain life events) Insights in customer behavior based on transaction data (e.g. demographic spread, shopping patterns, share of wallet) Supporting clients to increase revenue is a perfect opportunity for banks to show their industry knowledge as a unique selling point and intensify the relationship with the client. It makes their advice to their clients more meaningful and thus contributes to higher cross and deep sell. Several of our clients are already working on these kinds of propositions. There are interesting examples outside the financial industry on how to improve your client s business, see the box on the next page. 6

9 2. A shift in focus: Data as a service for clients Examples from other industries on how insights improve the business of your client Amazon uses purchasing data to propose alternative products to a client based on what other clients with similar interests have bought. In the same way banks can analyze transaction data for merchants to provide them information on the main competitors where their clients shop or how much they spend. A Dutch online retailer analyses all false-hit results and finds out which product is eventually bought by the client. This information is used to optimize searches for clients. Eneco combines energy use with demographic data so that customers can compare their energy spend with similar households. In this way they help customers manage their budget as well as contribute to sustainable energy use. Bundle uses data from the US government, from aggregated, spending transaction from banks and other sources to derive personalized recommendations on restaurants, bars, and shops. Consumers can find out what type of people go to a place, how often people go back, and get recommendations on places they are known to like through analysis of their spending. (Source: Economist, Deloitte Big Data Team) 7

10 3. Added value of Data as a service Is the move from data to insight also big value? What is the real potential of Data as a service for clients of banks? New business models and pricing mechanisms will need to be explored to capture the value of these new services. Otherwise, the value will be more indirect: differentiation, deeper loyalty and increased cross and deep sell The value of Data as a service Value of the internal focused propositions is evident as a result of the banks efforts over the past years. The success of the business model of Data as a service ultimately boils down to the value it generates for clients. To what extent these services address the clients needs will determine whether or not banks will be able to capture the added value of external data services. These Data as a service propositions (quadrant 3 and 4 in Figure 1) generate both qualitative indirect value as well as quantitative direct value for banks, having the potential to: be a differentiating factor in retaining the client and consequently also securing existing revenues; provide the means to really integrate with the client s business as trusted financial advisor; be charged a premium price based on the added value characteristic and therefore directly generate revenues; increase the share of wallet, generating capital as well as cross sell opportunities. New revenue models are required The challenge for banks is that clients will see these services as new propositions and not simply an enhancement of existing products or services. It is there crucial to think about more innovative ways to price these services to clients. We see a few opportunities to move away from the classical transaction driven pricing: New services will have a subscription fee, paid periodically to gain access to the data and analysis that are provided by the bank. This seems reasonable for propositions such as Direct debit scan, Opex/Capex cost reduction advice, and Personal banner marketing for own clients ; A different approach is to let the fee depend on the costs saved or revenue realized by the client. This business model intensifies the relationship between bank and client even more, although it is harder to measure and therefore more suitable for large corporate clients, comparable with cash management services. This can be used with regards to the Financial Supply Chain Management Tooling cash flow analysis ; Other smaller propositions that relate to a trusted financial advisor will be most seen as retention tool and can therefore most probably not be priced separately. Figure 2. Potential added value and ease of implementation The potential added value for clients is highest for direct marketing and the use of their client ratings, as stated in figure 2. The most valuable potential offering in our view is the one regarding direct debit scans. To illustrate this, given the fact that in the Netherlands the yearly total of default amounts approximately 12 billion, providing the direct debit scan for corporates and merchants on a fee-base can generate a significant additional income. 8

11 4. Unlocking value means locking into commitment We are convinced that the added value of transaction data will need to be unlocked. Banks executives will need to recognize the strategic importance. Unlocking the potential needs strong executive commitment. A lean approach will help organizations to focus only on the data needed to deliver specific business outcomes. Rather than rushing into large data programs organizations need to invest in capabilities across people, process and technology. Some important conditions have to be met to facilitate a successful approach To successfully take the necessary steps it is essential that certain important conditions are met. Centre(s) of excellence with data talent and business expertise Positioning the Data as a service function within the organization is key, due to the cross-functional, knowledge intensive and dynamic character. This includes deciding upon the design principles like whether or not to consider it a Centre of Excellence or as a unit within the IT or business department. Either way we suggest to position the Data as a service function on a strategic level with mandates across IT and business functions. Key to success will be capable people who see how data insights can enrich customer propositions and create business value. It is not about technology, but understanding of customers and business models. Here a combination of data specialists and business people with the right data driven mind-set are crucial in extracting the value from the data at hand. Banks should acknowledge that these are specific and scarce talents and be critical on whether or not these capabilities are present in the current IT- and Business Intelligence departments. A lean approach focusing on concrete business results A suitable IT infrastructure is needed to facilitate the necessary consolidating of different internal and external data sources, and perform the required analysis. Procurement of a proven analytics tool is only the tip of the iceberg. Key challenges will be encountered in setting-up an integrated data warehouse across product departments to unlock the necessary data. Also data collection needs to overcome privacy and security implications related to direct and indirect use of the different data sources. Build executive commitment Executive buy-in is the most important precondition to successfully launch Data as a service. Obtaining C-level sponsorship requires a clear vision and business case but foremost compelling business pilots. Start small should be the motto. The first concrete results subsequently can be used to gain broader C-level sponsorship. A lean approach to data services only focusing on the data really needed to deliver added value- will help to convince senior management. Sharing success stories from other industries (e.g. through a site visit) will help in opening the mind to see the potential. Data programs become fat due to the ill-advised attempts to capture, store, and analyze even greater volumes of data. With respect to the data and IT infrastructure the lean principal therefore also applies. Start with a clear vision on your overall data architecture and scale based on business requirements and results. When there are existent large investments in renewal of IT systems, it is important to make sure that integrated data analysis will be incorporated upfront. Adding this to the IT requirements will save a huge additional effort in a later stage. 9

12 4. Unlocking value means locking into commitment Once the infrastructure and data is made available, the business tends to start requesting excessively. Therefore it is key to align analysis requests with strategic business objectives. This ensures focused results and consequently drives a more strategically embedded capability. Client s trust and compliance: privacy by design From experience, it is known that three subjects cause the majority of the concerns when engaging with analyses on personal data: There is a rule that states that personal data may only be processed for specified and explicitly stated purposes and that these purposes have to be communicated to the consumers. At the time the transaction data was first collected, processing it for value creation may not have been an intended named goal There also must be a formal ground for processing; the reason why using the data for the intended goals is allowed at all. Usually this can be found in the consent of the customer or if the bank that holds the data has a legitimate interest to process it. The legal ground should be given proper attention and selected carefully, to avoid pitfalls in the future It is important to realize that human decision making should always be part of the process. It is currently forbidden to take automated decisions based on profiling which has a direct influence on a customer and it is unlikely that this ban will soon be lifted regulations. At the same time this will give the opportunity to embed the bank s point-of-view towards safeguarding its role as a trusted advisor; 2. Consult an expert: privacy regulations differ between countries and can be very complicated, even counter-intuitive; 3. Control your data process including data flows: be sure to know which data is used by whom and where; manage the authorizations accurately. In our opinion as stated before, the legal constraints regarding privacy are usually not the blocking issue towards using transaction data for propositions like Data as a service. It s is the bank s role as a trusted advisor that really demands a framework of well thought-through measures to safeguard any potential misuse of data and clients cause of distrust. In order to comply with the requirements, address the concerns and to safeguard the client s privacy and unintentional privacy breaches, when dealing with personal data we suggest a three stepped approach: 1. Privacy by Design: take customer trust and privacy and the rules regarding data protection into account from the very start of your idea. This will allow for creativity on ways of data gathering, anonymization and utilization necessary to overcome the hurdles that may be imposed by the rules and 10

13 5. Road to success The road to success requires a structured approach, driven by the strategic priorities. The three most important conditions for a successful approach are the executive buy-in, the capable people to make it work and the IT infrastructure to facilitate the ambitions. Competition on Data as a service propositions is emerging quickly, so banks must act now to seize this opportunity themselves. The road to success requires a structured approach, driven by the strategic priorities To successfully generate new Data as a service propositions a structured approached is necessary. This approach should include the following steps: 4. Perform pilot: Start small to realize concrete results as soon as possible. This will help in attracting C-level sponsorship, which is needed for future investments in these propositions. Furthermore, examples from other industries show that the companies that are successful in data analytics propositions are usually the ones that provide room for experiments and piloting; 5. Prioritize and implement: Select the successful and high value adding propositions and implement them. Banks must act now! Although the act now statement may sound like an old tune, with respect to Data as a service it is actually true. The main driver is that waiting will result in other companies grasping this opportunity. This is already happening to a certain extent, with several large corporates outside the financial industry analyzing transaction data themselves. Furthermore there are examples of financial institutions that currently are trying to make the most out of their transaction data. But banks should be stimulated the most by the fact that it is not inconceivable that third parties will gain access to the bank s transaction data and instead become the first movers. 1. Identify opportunities: It begins with the decision makers asking crunchy questions to identify opportunities and define strategic priorities based on crunchy questions (e.g. to what extent do we currently use our transaction data to bring insight to our clients. Is this a strategic topic in our discussions to create more added value and trusted relationships?); 2. Determine data sources: Asses the currently available data sources and data infrastructure. Determine which data sources can and will be used; 3. Design the data analytics process: Define the process from setting business requirements to extracting and analyzing data to interpreting the results and translating to actions; 11

14 Contact Hans Honig Partner Marylka Zwiebel-Klaeijsen Senior Manager Leonne Jongejan Manager Wouter Koot Manager Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Deloitte provides audit, tax, consulting, and financial advisory services to public and private clients spanning multiple industries. With a globally connected network of member firms in more than 150 countries, Deloitte brings world-class capabilities and high-quality service to clients, delivering the insights they need to address their most complex business challenges. Deloitte has in the region of 200,000 professionals, all committed to becoming the standard of excellence. This communication contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collectively, the Deloitte Network ) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this communication For information, contact Deloitte Touche Tohmatsu Limited.

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