TRANSFORMING BANKS WITH DATA INTELLIGENCE

Current digital age requires banks effectively manage information by consciously learning from its experience and feeding this learning into its strategic development agenda. The data can be explored to its full potential only if the entire data lifecycle is properly managed: from data collection, recording, monitoring and cleaning to visualization, analysis and interpretation. Formalization of the data strategy is necessary to reinforce the importance of data as a fundamental banking asset. This article talks about how banks can manage and use data to keep its business efficient and scalable.

As commonly referred to as a new commodity, data has characteristics that are similar to traditional commodities, for example scarcity. Unlike the information, which is abundant, the data is scarce. The information needs to be collected, recorded and safely stored first in order to become data. It can be then processed to benefit the data owners, however the entire process of transforming information into data is cumbersome, making data a scarce but important resource. Data, as a precious asset, adds significant value to the bank’s profitability and sustainability and hence deserves a separate line on the balance sheet, along with loans, investments and cash. 

Banks already have tremendous amounts of data about clients and transactions recorded in their systems, such as bank ledgers, transactional history, channel traffic. Beyond this, the rapid economic digitization offers various external sources of data, which banks can tap into, such as mobile operators, geospatial data, credit bureaus, social networks, online behavior. Many banks leverage such data to the best potential, while others still developing the proper data strategies. With the use of modern tools, financial institutions have an opportunity to effectively use data and transform it into intelligence, necessary for making smart business decisions, staying competitive and profitable. The banking sector faces ever growing competition from non-traditional players, hence need to look for the effective instruments to expand revenues and manage costs. Data is one of such instruments and can help banks to: 

(1) drive profits:

• Increase wallet share
• Cross-sell through targeted marketing campaigns
• Automated credit scoring using traditional and alternative data

(2) manage costs:

• Churn prevention
• Minimize costs of serving the customers,
• Reduction of marketing costs
• Early warning signals, fraud prediction

KEY PILLARS OF THE DATA STRATEGY

To exploit the maximum benefits from data, financial institutions should have clear data strategy, which combines three fundamental pillars:

(1)    Data management combines all aspects of the data ownership and governance, including data collection, storage, structuring, review, cleaning and monitoring. Effective architecture (cloud / on-site servers, data warehouse, data lakes), software (integration of CBS, CRM, LOS and other systems, analytical and visualization tools) as well as organizational structure (a dedicated data unit) should be in place to optimize the use of data in the next two pillars.

(2)    Reporting and visualization as next fundamental pillar of the bank’s data architecture, which uses the product of the first pillar to create an intelligent view on the current state of business. Various reports and dashboards help business managers to track bank’s performance, quality of the portfolio, staff productivity and other categories necessary to drive the business.

(3)    Data analytics as a most challenging but valuable part of the data strategy, which yields the highest returns on investment. Analytical models are developed by banks to predict customer behavior, better understand their preferences, proactively manage the overall business.

A clear data strategy around these three pillars would help banks identify the gaps and prioritize the initiatives aimed to improve the use of data. The question remains who is responsible for developing such strategy. Banks sometimes dismiss an importance of the dedicated business intelligence unit (BIU) and have analytical cells scattered across the institution, however having a centralized ownership of the data ensures that someone assumes a complete responsibility for the data and serves as a single source of truth. Given an importance of the data asset, such unit must have a significant weight in an organizational structure of the bank, i.e. governed either by the senior manager or has direct reporting to the C-level management. Moreover, the staffing of such BIU requires experts with technical skills, as well as business knowledge and experience, hence it is ideally comprised of various types of individuals with diverse backgrounds.

Finally, a bank must ensure that insights gained from data analysis are effectively used to improve desired business outcomes. Besides the three pillars described above, BIU and the bank management shall also be mandated to initiate and execute a cultural shift within the institution to ensure the ultimate users from across the bank use data insights for daily business decision making.

USES OF DATA ANALYTICS

Data analytics aims to provide intelligence to better understand the market trends, monitor performance and customer behavior. Forward-looking analytics enables banks to predict future events, such as customer default, attrition, propensity to buy specific product, etc., and to undertake effective actions to stay ahead of the competition, offer targeting products to the right customer segments or allocate funds more efficiently. 

Customer analytics is a powerful instrument aimed to address ever growing market competition. Customer management requires in-depth customer knowledge for targeting and acquisition, sales strategy, relationship deepening churn prevention and lifecycle management. Data is used along the entire chain of customer lifecycle to improve sales force effectiveness and manage the customer servicing costs.

Customer targeting / segmentation is the first step in a proper customer management to gain a clear vision on key segments of its portfolio. Depending on the nature of the segmentation, various determinants or their combination can be used (i.e. company size, annual sales turnover, loan size, assets, number of employees). For the banks with large portfolios, this may require advanced data science expertise and significant processing power. In addition to basic portfolio segmentation the banks may introduce additional layer of value-based segmentation to allow for more tailored customer management techniques. Customer activation and churn prevention models identify the patterns of clients’ usage of various products and services through behavioral analysis across products and channels. It helps differentiate between engaged and non-engaged clients and launch pro-active reactivation campaign in a timely manner. Product cross-sell modeling represents a set of statistical algorithms designed to help banks understand the customer behavior along customer life cycle and build deeper relationships with their customers. These models include product propensity scoring and next best action. Such models help understand whether a particular group of customers have a higher propensity to purchase a particular product given its demographic, financial and behavioral characteristics. Such analysis should be closely coordinated with the marketing department for potential marketing campaigns. Risk management. Credit and risk management analytics is a critical part of banking and includes application credit scoring, collection and recovery scoring, is crucial for the automated and fast decision-making. The data can help the bank to mitigate the risks of cost overruns for customer management. By utilizing the data for optimized marketing campaigns, timely ads, automated outreach and customer onboarding, the bank can reduce excessive costs of servicing in times.

CRITICAL STEPS TO SUCCESS IN DATA ANALYTICS

Banks, looking to effectively leverage data for business growth, can explore the following steps:

(1) identify key stakeholders at bank who support the development of a clear strategy for data analytics and instituting the right resources to operationalize the strategy;

(2) ensure that the IT infrastructure supports the collection and storage of reliable data including data warehouses, appropriate data collections and management instruments and integration with third party platforms;

(3) create a centralized analytical unit with a specialized team proficient in the various fields (i.e. data management, data visualization, statistical modeling) to orchestrate and drive results;

(4) develop the strategy to utilize data and communicate findings (i.e. formal communication channels, dashboard creation, identifying champions across business units for continuous feedback);

The bank can also benefit from establishing external partnerships. Advanced analytics might require outsourcing parts of the process to the third-party vendors, for example, platforms specializing in social media analytics, speech recognition / NLP, geospatial, log analytics, marketing analytics, risk and collection, data management and digitization. Partnerships between financial institutions and fintechs can create a synergy by combining the scale and resources of financial institutions and innovative knowledge and advances algorithms of the fintech companies.

In conclusion, in the word of multi-faced competition, banks can gain significant dividends from having a well-articulated data strategy and benefit from having an access to the extremely valuable data to drive business growth. 

Disclaimer: the views and opinions expressed in this article are those of the author and do not necessarily reflect the position of any agency, organization, employer or company.