In 2018, Gartner released a study that showed by 2030, 80% of legacy financial institutions won’t exist or be able to compete in any meaningful way.
Any financial institution wanting to remain competitive probably needs to focus on digital as a driver for other priorities.
Impact of Digital on the Financial Services Sector
The financial services sector is probably the most data-intensive sector in the global economy, and the impact of big data really can’t be overstated.
Banks have enormous amounts of customer data (i.e. deposits/withdrawals at ATMs, purchases at point-of-sale, payments done online, customer profile data collected for KYC…), but due to their siloed, product-oriented organizations, they’re not very good in utilizing these rich data sets.
Due to the increasing and changing customer expectations and the increased competition of fintech challenger players, legacy financial services businesses cannot leave these huge amounts of data untapped.
Instead, banks and insurers must leverage the existing (and new) data sets to maximize customer understanding for a competitive advantage.
Big players with large R&D budgets are already creating compelling use cases, but many organizations are still lagging behind. They are at risk of being disrupted by the digital-first challengers.
Digital Innovation Drivers
Innovation in financial services is driven by several factors, which amplify each other—resulting in an exponential increase of data and the need to derive value from it:
- Significant changes in customer behavior and expectations:
- Customers are using digital platforms to interact with nearly every other category of service provider. They’re expecting the same from their bank or insurer, meaning that personal interaction is reduced, but at the same time it is possible to automate the collection of information and obtain more data about the customer (i.e. browsing history, geo-location data via mobile phone, exact timing of the interactions…) beyond the occasional branch visit. This data can be leveraged to compensate the reduced customer engagement caused by the loss of personal interaction.
- Customers use more and more social media: where these media used to be limited to closed private circles of friends, customers now use these media more and more in their day-to-day live, e.g. to interact with companies. This means banks and insurers should interact more through these channels to offer services and to gain insights about their customers.
- Customers expect more and more a high-quality, low-friction, around-the-clock, customer-centric experiences across multiple channels. In order to deliver personalized services, an in-depth, holistic knowledge about the customer is required. This can only be achieved by leveraging all available customer data.
- Technology evolution leading to larger amounts of input data:
- The rise of IoT (Internet of Things) will further explode the amount of customer data, as it will result in new, continuous (even if customer is not interacting with the bank or insurer) streams of data.
- New advanced authentication techniques, such as biometric authentication and continuous authentication (e.g. mouse movements and keyboard rhythm or accelerometer and gyro sensor readings on mobile phone), will also considerably increase the amount of data to be processed in near real-time.
- The rise of Open Architectures (Open APIs), allows banks and insurers to collect valuable data about their customers, even from data stored at competitors.
- Competition of fintech players for new financial services. Demonstrated by the recent success of fintech robo-advisors offering automated digital investment advice using gathered customer profile information shows how fintechs are already better at converting data into new, compelling customer services. Unless banks can deliver quickly similar services, they are likely to lose considerable business to these fintech challengers.
- Regulatory pressure: the recent tsunami of new regulations at global scale (Basel III, FRTB, MiFID II, AML/KYC, FATCA…) force banks to disclose more diverse data and more granular data to central banks and regulators. Furthermore, the fines associated when not complying to these regulations are climbing. This forces banks to collect more and more data in a controlled way, so that the necessary regulatory reporting can be generated automatically, and also that all data is available for ad-hoc inquiries of the regulators.
- Increased cyber-security: with fraud and financial crimes increasing, banks need to protect their most valuable asset, namely the “trust” that customer give to their bank. This increases the pressure to further secure the interaction channels and the customer data, through different security techniques. One of the most promising is risk-based authentication, in which a fraud-detection engine calculates a risk profile for each channel request, determining the required level of security (authentication). This fraud-detection engine uses customer analytics to identify irregularities in the user’s behavior.
- Pressure to reduce operational costs: due to the increased competition and low interest rates, profit margins in the financial services industry are dropping. Banks and insurers are forced therefore to reduce operational costs, by improving business efficiency. Many of these efficiency gains can be driven by the insights gained from big data.
- Technology evolution to support the processing of huge amounts of complex and diverse data in real-time: with data sets growing so large and complex, traditional tools are no longer able to process this data at sufficiently low cost and in reasonable time. New technologies provide an answer to this issue, allowing to process these data sets in near real-time and at lower costs.
- Of course, blockchain-based information governance to connect siloed data stores within organizations and across multiple partner ecosystems.
- API-based integration and extension of existing enterprise software platforms
- Cloud solutions: cloud solutions offer a cheap and flexible (i.e. elastic scalability) infrastructure (but also higher-level services) to support these digital initiatives.
Digital Innovation Use Cases
With financial services already being one of the most data-driven industries, digital innovation enables a multitude of use cases, especially those where data and customer analytics can bring added value.
Sales and Marketing
McKinsey research estimated that sales and marketing consume about 15 percent of the costs of financial service companies, meaning that improving the efficiency of these processes can lead to significant cost savings.
When looking at the customer lifecycle, we can identify 3 stages, i.e.
- Acquisition: the art of attracting new customers, by targeting prospects through campaigns.
- Activation: this stage includes re-engaging old customers or dormant customers to buy new products/services, but also the art to maximize the first purchase of new customers.
- Relationship management (Cultivation): cultivating the customer’s relationship with the company.
These stages can be supported by digital, demonstrated by below use cases:
- Improved efficiency of acquisition through data: mass marketing campaigns are costly and often ineffective, especially nowadays when customers are overloaded by marketing campaigns through different channels. Effective marketing campaigns should therefore target the right set of customers, with the right (personalized) message and through the right channel (direct mailing, email, channel advertising, social media, TV, radio…). Digital can provide an answer to this by:
- Segmenting prospects based on publicly available information about the prospects and insights gained from information from the existing customer base. This allows to select the right set of prospects for targeting a new product/service (hyper-targeted marketing).
- Determine the best channel (mix) for the marketing campaign, based on the gained insights in the selected prospect segment.
- Personalization of marketing messages (contact optimization)
- Monitor what customers say, i.e. monitor social media and other information sources (e.g. call center records) to get direct feedback on marketing campaigns, allowing to adjust ongoing campaigns or take lessons for future campaigns (and product design/development).
- Identify influential customers, i.e. identify and engage with influential customers (i.e. customers who have high impact on company brands or products) to boost word-of-mouth marketing.
- Improve the efficiency of activation through digital: once a prospect has replied to a campaign, it is important to maximize the first sales opportunity. At the same time, sales to existing customers should also be boosted. Digital can also support those processes through:
- Segmentation of customers, based on the available data (e.g. customer profiling, analyzing transaction patterns, past and immediate customer behavior…) to get real-time customer insights. This helps predict the products or services customers are most likely to be interested in (i.e. predictive analysis) for their next purchase, thus helping to determine next-best-offers (next-product-to-buy) and what the customer’s most likely next action will be. These products can be specifically marketed to the customer and proactive offers can be generated.
- Bundle products based on the gained insights to boost cross-selling.
- Optimized pricing, i.e. apply dynamic pricing, based on estimation of how much a customer is willing pay for the product or service.
- Generate cross- and up-selling opportunities based on customer insights and current customer behavior. These opportunities can result in notifications, call-backs or in specific pop-ups in the front-end channels, e.g.
- Customer purchases a particular type of asset or their portfolio represents a defined model of various asset classes and risk tolerance: investment cross selling opportunity
- Customer has received a large inflow of cash: investment cross-selling opportunity
- Customer has less money on his current account than he requires based on the budget he has created in the “Personal Finance Management” module of the bank: credit line (overdraft) cross-selling opportunity
- Customer has been simulating car loans on the internet and steps into a branch: car loan cross-selling opportunity
- Customer has cancelled a demand in middle of the flow and steps into a branch: cross-selling opportunity for continuing the demand
- Customer has an expiring term deposit: term deposit reinvestment selling opportunity
- Customer arrives in foreign country (identified based on geo-location information): opportunity to authorize credit card for the country (if not the case) and to temporarily increase his credit limit (e.g. to pay hotel bill)
- Customer is performing home renovations (i.e. identified based on transaction information): renovation loan cross-selling opportunity
- Customer is buying a bond on the market: upselling opportunity for similar structured note (if customer insights show that customer would be open to this and has enough knowledge of the product).
- Customer is requesting consumer loan, but payment history identifies lot of home renovation expenses: upselling opportunity for home renovation loan
- Customer does not have a home yet and is currently located at a house for sale (based on geo-location information and public information of houses for sale): selling opportunity for mortgage
- Customer modifies certain customer information (e.g. change of address due to move/relocation, change of civil status e.g. following a wedding): selling opportunities for loans (e.g. mortgage, car loan…) or insurances (home insurance, car insurance…).
Digital can enable use cases to optimize the management of an existing customer relationship, i.e. the so-called customer cultivation by:
- Transforming the business to a customer-centric business: empower employees with a single customer view (i.e. 360° holistic view of the customer), providing a broad, centralized view of the customer information, a full view of the history of inquiries and transactions (regardless of the interaction channel) and insights on the customer’s family, business and bank employee relationships. These insights allow to have more focused and in-depth interactions with the customer.
- Identifying high-value and most profitable customers: identifying those customers allows to provide a premium service to these relationships, i.e. offer more attractive products and services, provide attractive pricing or get insights on how they behave, how they best should be reached and what motivates them to buy more.
- Enhancing the loyalty of existing customers through:
- Targeted next-best-offers
- Loyalty programs, e.g. based on card usage habits
- Partnerships with retailers to send discount offers to cardholders, who use their card near the retailer’s stores
- Retention management: detect customers with high risk of leaving (indicators can be e.g. cancellation of automatic payments, customer complaints in call center calls or on social media…) and provide retention offers to these customers.
- Adaptive channel interactions: based on the data of current and past customer behavior, it is possible to predict future customer trends and what their most likely next action will be. Front-end portals could use this information to show dynamic buttons/menu items (i.e. dynamic screen adaptation), which put these next actions forward.
- Improve efficiency of products, services and channel interactions: monitor the customer journey, i.e. interactions of customers, to gain insights to improve existing channels, processes and products. Such improvements will also result in an improvement of customer service.
Digital can help banks and insurers to significantly improve risk management, through improved and (more) real-time insights in the customer behavior.
This paragraph provides some examples per type of risk:
- Cyber (identity) fraud detection and prevention: use data to feed fraud-detection engines, allowing to continuously assess the risk of identity fraud and determine near real-time whether additional security measures (e.g. additional authentication techniques or restriction of access) are required.
- Liquidity risk management: get better insights on the incoming and outgoing cash flow, to optimize liquidity management. This technique can be useful for both physical money at branches as for the overall liquidity management of the bank/insurer.
- Credit risk management: based on customer insights, improve the credit models for private and corporate customers, thus allowing to improve credit scoring. These insights can be derived from transaction history, public information (e.g. annual reports of companies), IoT data (e.g. inventory sensors, home sensors, car sensors…)… This data can also be used to better manage the collateralization of credits, thus also reducing credit risk for the bank.
- Card fraud detection: analyze the card transaction patterns (location, timing, amount, type of merchants…) to identify fraudulent transactions, so that they can be blocked.
- Insurance fraud detection: improve detection and prevention of fraud at opening of a new insurance policy (i.e. policy data not matching with reality) and fraud when introducing claims. Several indicators can be used for this identification, e.g. IoT data (sensor data can give directly information about car accident or home damage), customer performing several simulation attempts with different configurations before demanding an insurance policy, a high number of view request to the insured amount of life insurances or fire/theft insurances preceding a claim, unpaid premiums…
- Legal Claim Management: Digital can also help to better avoid, prepare for and react to legal matters involving large amounts of data, e.g. collect all information related to a legal matter, assessment of cases to determine probability that case will lead to a legal claim, tracking of regulations to avoid fines and sanctions.
New Data Driven Products and Services
Digital allows also to deliver new, innovative products and services to customers, which use the insights derived from the data streams.
- Home insurance in combination with IoT (utilities smart meters, smoke and carbon monoxide detectors, fire suppression systems, advanced alarm systems) allowing to improve protection, dynamically adapt pricing and provide value-added services (e.g. statistics on utilities consumption).
- Car insurance in combination with IoT (black-box in car) allowing to improve protection (e.g. car recovery in case of theft), dynamically adapt pricing (based on driving style) and provide value-added services (e.g. fleet management services to SMEs).
- Trade Finance contract in combination with IoT (supply chain sensors) allowing the automatic execution of the contractual conditions defined in the contract.
- Personalized Wealth Management Advisory: use data to identify customer goals, family situation, risk aversity, financial situation, financial goals… and propose automatically investment advise, tax advise and financial planning based on these insights.
- Personal Financial Management: use data to automatically classify financial transactions in categories, which are best suited to the type of customer, propose budget plans in line with customer’s goals and compare budget plans and actuals with “people-like-me”.
- Algorithmic trading: analyze massive amounts of market data in fractions of a second to identify investment opportunities
Internal Management Support
Where all use cases up till now are all focusing on getting better insights about the customer and consequently better servicing these customers and generating more profits, digital can also be leveraged for internal management decision support.
- Examine customer feedback: collect and analyze customer feedback from different sources (e.g. call center comments, social media…) to identify improvements to products and services. Using these techniques allows much faster reactivity to this feedback than traditional surveys or focus groups, which tend to be slow, costly and inaccurate (due to the limited size of the sample group).
- Determine branch location/relocation strategy: use data to understand where customers live, where they shop and how much they spend to determine optimal locations of branches.
- Compliance efficiency: Data can also be used to better comply with regulations and improve regulatory reporting. Some examples of regulations which are very data driven are KYC/AML, FATCA, MiFID2 and Basel III. Getting better insights into the customer will allow banks to reduce the risk of sanctions and fines, due to regulation breaches.
Digital innovation can support multiple use cases which will bring significant benefits for banks, insurers, and other financial services:
- Drive growth in the business
- Drive better risk management (better identification, assessment, prevention and mitigation of risks)
- Reduce and better control costs
- More personalized and targeted marketing (maximizing lead generation potential)
- Improved measurement of marketing effectiveness across all channels
- Optimized funnel conversion
- More personalized customer servicing (relevant content per channel, dynamic pricing, next-best-offer…)
- Faster reactivity (seconds rather than hours) to customer related transaction issues
- Holistic and forward-looking view (predictive analysis) of customers
- Increased customer loyalty (better servicing, loyalty programs, knowing which customers are going to churn and when)
- Enhanced usability (by dynamic adaptive front-ends and better monitoring and feedback cycles on usability)
- Identify hidden connection between seemingly unrelated data
- Identify customer trends and changing customer preferences and expectations faster than competitors
- Guiding customers to low cost channels
- Reducing time lost searching information
- Supporting optimal management decisions
- Improved modeling of credit scoring and fraud detection
As we can agree, there are a LOT of benefits to be unlocked with the right digital innovation program.