Data is the New Growth Currency

Analytics is at the core of innovation and differentiation in payments.  With the growth of digital payments, banks have a plethora of data available from an increasing number of digital payment sources including issuance and acquiring systems Advanced analytics can unlock patterns hidden in massive data flows.

Connecting the Dots

Banks have the benefits of large customer bases and access to rich transactional data. They know where, with whom and when customers are spending money.  The sheer breadth of data sources multiple channels, product silos, and disparate touchpoints however requires banks to build a holistic customer view.

Combining real-time, activity data from multiple digital channels with offline channel data; uniting anonymous customer information with known customer identification data; aggregating real-time customer activity with historical data presents challenges due to the sheer volume, variety and velocity of data and the fact that the data is resident in multiple databases

To meet the challenges facing them, financial services need a unified data platform that converges all data into a data fabric that allows them to store, manage, apply and analyze data with speed, scale, and reliability. Banks are increasingly placing greater emphasis on converged platforms capable of running intelligent applications.  New data management platforms are capable of not only capturing and storing enormous volumes of structured and unstructured data, but also creating a global data fabric that enables simultaneous analytics and applications.  As banks with more mature data science groups push analytical boundaries, machine learning and predictive analytics will continue to receive a great deal of attention.

Mining Data Profitably

Most financial service providers offer a variety of services to customers and merchants. Integrating different sets of transaction data into one centralized payment platform offers transformational opportunities to help banks find new sources of growth, optimise marketing and business operations and even find new business models, making financial institutions smarter, agile and more competitive. This single data store could fulfil the needs of multiple business pursuits including:

Optimising Marketing Performance

Financial institutions can improve engagement and drive marketing planning and performance by gathering data from the customer and the merchant side.

Deliver Data-driven Payments Experience  

Most customers have a multi-product, multi-channel relationship with a bank, presenting unprecedented opportunities to design meaningful; contextual experiences and to create a lasting, long-term relationship that builds loyalty. Financial institutions can harness large quantities of consumer data to create customer segments and deepen engagement using a multitude of attributes.  These include:

• Transaction Indicators: Glean customer interactions over multiple channels (i.e., online, in-store) and existent and new payment methods such as dematerialized cards held on digital wallets or in the cloud,
• Lifestyle Indicators: Analysis of account balances as well as spend patterns
• Conversational Indicators Incorporate unstructured data from their customers’ social media profiles, call center conversations, social data from Facebook and Twitter, clickstream data from websites, voice logs from call centers, communication data from e-mails
• Credit score Indicators: Data harvested from public or “pay” Web sites (e.g. FICO credit score).
• Financial Indicators: Account balances, long-tenure deposit accounts and other portfolio holdings with a bank

The analysis provides marketers a powerful tool to become more closely aligned with individual preferences, creating a customer relationship that is shifting from a “nice to have,” time-sensitive offer-based relationship to a “must have” digital companionship based on deep insights and understanding of the consumer. Banks can be used to map customer transaction journeys, draw patterns to optimise product performance and develop new product offering as well as deliver customized or individualised real-time offers, at the right time, with the right message, to influence a customer’s decision making.  For instance, deliver next-best action engagement or in-the-moment optimized offers such as real-time credit to eligible customers at check-out or upsell products to customers at the close of a loan tenure.

Deepen Merchant Engagement

Another vector of impact is the way acquirers can exploit transaction trend data to improve the quality and profitability of merchant portfolio. For example, acquirers can glean transaction patterns across merchant categories, instrument type and interchanges. This can help in scoring individual merchants based on transaction recency, frequency and monetary value, which in turn can inform strategies related to acquisition, promotions, pricing and retention.  Additionally, information on payment types can be used to improve share of on-us transactions by advising customers on which instruments to use or collaborating with merchants to make special offers.

Acquires can offer analytical intelligence to merchants to improve decision-making and business performance, as a value-added service. Visibility into category-wide transaction trends can help merchants benchmark performance against aggregate trends. Or tweak marketing offers based on customer transaction insights.  For example, a Pan-India study on Payment Gateway conducted by FS indicates the volume of transactions on Friday is higher than weak days

Modelling Optimal Quality of Service  

An ability to collate and analyse data from critical customer touchpoints – ATMs, and POS terminals–  aids banks in optimally modelling system and transaction performance of individual or groups of ATMs, with a view to improve service efficiency. Essentially banks operations teams can use the data for:

• Location Management: Track customer footfalls and transaction performance for each ATM terminal to optimize network planning
• Incident Management: Continually monitor and rapidly identify and isolate, application, network and third party or host connectivity issues impacting transaction performance, improving service uptime
• Customer Management: Insights into customer transactions by function such as withdrawals, deposits, inquiries, bill payments, transfers and other services. For instance, financial institutions can quickly understand exactly which payments were being processed by the impacted system at the time and the client’s that submitted them. Which means the company can proactively contact impacted clients, potentially before the client even realize their payment has been delayed. In addition, banks can track “on us” versus “off us” transactions across the network
• Cash Management: Track cash positions for withdrawals, deposits, account transfers, failures, approvals, declines, force posts, reversals on any ATM. Further AI-driven tools can proactively detect likelihood of cash outages across ATMs and combine this with route-optimization techniques to save money.

Managing Risk and Fraud

With the advent of new payment forms, there is a concomitant intensification of fraud risk. Newer regulatory and compliance requirements, fraud and anti-money laundering preventive steps are placing emphasis on stronger governance and risk management.  With fraud schemes and the sophistication of fraud perpetrators constantly evolving, analytics tools equipped with powerful machine learning algorithms allow banks to combine data streams from the issuer and the acquirer side to contain the fraud loss.  Potential applications include:

• Risk Assessment and Management: Discern risk patterns from payment data feeds and hone the accuracy of risk assessment and scoring rules on an ongoing basis
• Risk Identification: Flag anomalies that indicate potential fraud — for example transactions from different locations using the same instrument or payments in violation with normative transaction patterns of customers

Generating Monetization Opportunities

Creating newer business models or frameworks that leverages the available data allows financial institutions to generate new monetization opportunities— for example, sharing -analytics capabilities with new ecosystem partners including merchants, customers, third-party payment service providers.

Banks, for instance, could offer customers a risk exposure score as an added value service. Likewise, banks supplement the information and use customer profiles to provide feedback to retailers – for example what comparable households spend on specific categories. Most merchants can do this too, but only based on information on their own customer. Banks, by contrast, cover the entire market.

Further open banking and  the adoption of PSD2 marks a new growth trajectory for banks. PSD2 is playing a pivotal role in the market, giving customers full control over their money as well as giving third parties access to their accounts.  Progressive banks would expose APIs allowing partners to build new use cases and services rapidly.

Action Plan for Banks

The above examples illustrate that consolidating data across the bank offers excellent opportunities for introducing new business models, and for implementing major innovations in both the front and back offices of banks. Many banks are still investing in new systems and integrating analytics into their daily operations, optimizing and developing best practices. To ensure a return banks need to:

• Prioritize areas of focus: Identify specific areas where data and analytics can have the greatest impact and obtain leadership engagement from the start. then, should be to develop an integrated, enterprise-wide customer journey analytics ecosystem, in which internal and external data is continuously analyzed, delivering insights to all banking business units and stakeholders, across marketing, compliance, call center operations and digital services
• Start with questions, not with data: Banks tend to get caught up in looking only at what they can do with the information they have, which can limit thinking and approach. Ask chewy questions. For example: How can we increase customer transaction value by 20% by better understanding their individual interests and behavior, and considering a range of economic forecasts
• Develop a data-driven culture: Ensure the right/relevant data gets to the right department or executive in a timely fashion and there is cross-departmental collaboration between operations, marketing, finance and IT teams
• Select the right partner: Work with a vendor who understands end-to-end payments and can bring domain experience to bear on new proofs of concept and build on incremental successes

FSS Paynalytix, an advanced analytics solution, helps banks to efficiently model performance along multiple business and operational dimensions. The solution synthesizes data from multiple payment applications including POS, Mobile Banking, Internet Banking, Card Management Systems, Payment Reconciliation Systems and Payment Gateways to generate actionable insights for setting strategic direction, maximizing revenues as well as driving operational efficiencies. Intuitive data visualization dashboards provide on-demand access to data, making it easy for various business units within financial institutions to analyse operations performance, fraudulent transactions, channel profitability and the end-customer experience.