At a Glance
- As banks augment their digital capabilities, they need to integrate and easily access data that can provide powerful insights on business performance.
- Most banks, however, are lagging in this regard, because they spread data ownership and management among various functions and departments.
- Arming the CFO and finance group with more insights into company-wide metrics and operational performance data, access to the latest analytics methods, and expanded data engineering and science skills should lead to improvements.
- That will entail changes in the operating model and the role of the finance function, including greater participation in and co-steering of individual initiatives alongside the chief data officer.
As companies enhance the digital aspects of the customer experience, they face massive digital transformation needs. Companies in a range of industries have been using digital technologies on many fronts, including:
- consumer-grade, personalized experiences for employees and executives;
- real-time predictive insights;
- smarter automation to lower costs, improve service quality, and strengthen financial controls; and
- cloud enterprise resource planning to facilitate collaboration and flexibility.
Now, the Covid-19 pandemic has accelerated digital usage and created conditions for faster change than previously thought possible.
A key ingredient for this transformation is the integration and sharing of data that forms the basis for powerful insights. Better insights lead to better decisions, whether strategic or operational.
The finance organization has a major role to play, as it is increasingly called on to become a strategic partner with a deep understanding of each business within an enterprise. This evolution started with a focus on leaner processes. From spreadsheets in the 1980s to database management systems and integrated systems in the 1990s, web services in the 2000s, and process mining, visualizations, and intelligent automation in the 2010s, digital technologies have helped finance to raise throughput rates, speed up processing, and reduce error rates in transactional processes and reporting, while minimizing fraud risks.
Based on this track record, at some companies, the chief financial officer (CFO) and finance function take on a prominent role in processes and initiatives that rely heavily on data.
Deutsche Telekom, for example, established a CFO-led data and analytics capability overseeing a company-wide data lake, advanced analytics, and top data science skills. The company takes a cross-functional approach to budgeting, IT delivery, and team leadership, supporting different use cases on a common, scalable platform. Deutsche Telekom chose the CFO to oversee data because this role can guarantee a comprehensive view and provide relevant data at a high level of consistency and quality for all divisions. The approach has enabled Deutsche Telekom divisions to achieve their business goals faster and speed up execution on growth and efficiency targets—including a significantly better, more personalized customer experience.
Banks fall behind
In banking, Standard Chartered has worked with Dataiku, a data science and artificial intelligence platform, to enhance its financial reporting capability. Standard Chartered developed a data marketplace that employees across the bank can use, with product owners for every data set and a defined governance. On average, two people armed with the applications in Dataiku do the work of about 70 people limited to spreadsheets. Replacing spreadsheet-based processes with governed self-service analytics raises productivity by a factor of 30.
Most banks, however, are lagging in this regard. They spread data ownership and management among various functions and departments.
The lack of coordination causes several problems.
- Poor data integration. Without a view of data across business lines and functions, banks limit their opportunities to manage customer relationships based on holistic, up-to-date data sets. Wealth management and treasury, for example, both require a deep understanding of customer activities and behavior. Coordinating data and insights generation between these areas would provide more effective behavioral pricing and a view of aggregate risk exposure.
Within the treasury function of a European bank, it took up to four days until new business, captured by the relationship managers, became visible in the back-end systems, causing delays in assessing the aggregate risk exposure and liquidity needs. As a result, the bank had to demonstrate sufficient liquidity based on more conservative estimates, leading to higher opportunity costs.
- Inconsistent data. When a client is served by different groups and teams, supported by various platforms, the data that each group captures often does not match up with other data. Inconsistencies crop up with entity information at the corporate level, relationships between entities and individuals who serve on their boards, and address information. Such inconsistencies not only create work in the back office, but they also make for missed opportunities in cross-selling.
Take the interface between two groups, corporate clients and wealth management. Inconsistent data at the corporate level means that wealth management often misses a chance to actively address client needs at the individual level, such as cashing out after an M&A deal.
- Inefficient data usage. Currently, most banks limit the use of data to the team that sourced it, with no effective sharing of information, insights, or signals with other teams or divisions. For example, corporate banking relationship managers often have deep insights about their clients that could inform a comprehensive view of an industry. In particular, the insights could be used by the chief investment officer and risk or product managers in evaluating sector-specific risk considerations or product opportunities.
The CFO’s special position
In banking, CFOs do not only use data. Through their reporting and financial planning duties, they are able to identify potential inconsistencies or limitations in bank-wide data assets. This perspective makes the CFO a logical guardian of data.
Finance has the requisite technical capabilities and targeted business capabilities. Led by the CFO, the function can develop an independent view of the top and bottom lines in order to calculate key financial ratios, such as cost-income and return on tangible equity. Finance is in a good position to break down silos and advise business units, alongside the chief data officer (CDO), in prioritizing data initiatives that merit further integration and improvement. It can also provide forward-looking guidance to the business on revenues, liquidity, and strategic financial planning.
CFOs themselves aspire to a closer embrace of the data. Interviews by Genpact with more than 500 senior finance leaders found that generating actionable insights for the wider business and improving data management and governance were the second- and fourth-highest priorities, respectively, for finance in the future.
Moreover, a Gartner study argues that widespread adoption of cloud infrastructure will strengthen the CFO’s alliance with the CDO. By 2022, the study asserts, 30% of CDOs will partner with their CFO to formally value the organization’s information assets.
Granting the CFO and finance access to company-wide data, the latest analytics methods, and expanded data engineering and data science skills should lead to improvements in several areas.
- Business execution. A holistic view of revenues, cost, capital, profits, and risks across the organization (including customer segments, products, and regions) will allow finance to help shape the business and steer at the portfolio level. It could, for instance, help improve product offerings and pricing based on customer behavior.
- Forecasting. Predictive analytics based on rich data sets will enhance forecasting capabilities. One possible use case is better cash flow forecasting; another is more interactive business reviews through visual, immersive reporting and self-service tools.
- Profitability. A CFO’s understanding of profitability drivers at the client level can help the bank recognize patterns of success. Such an analysis would be built on causal relationships between, on one hand, financial investments by the bank in clients, new products, and platforms and, on the other hand, related returns from those investments. This type of root-cause analysis could support the business in applying similar patterns to other clients and markets, in order to increase overall profitability.
Fully exploiting finance’s potential to understand data and use it effectively would entail several changes in the operating model. First, a bank would need to enable greater participation in and co-steering of individual initiatives by the finance function. A leading European bank did this, in line with its CFO acting as copilot with the product head on the investment and sales committee. The change enabled more fact-based, data-driven decision making, which resulted in sustainable double-digit profit growth.
A second change would shift the focus of the finance function from minimizing costs to maximizing profits for the business. This requires investments in the latest technology, frameworks, and methods, plus skills to establish and run advanced finance analytics and services.
Finally, it’s important for the CFO to ally with the CDO, to oversee enterprise-wide initiatives aiming to improve data integration, data quality and integrity, and ease of access. These ingredients are critical to a bank’s increasingly digital customer experience and thus deserve senior leadership attention.
CFOs have an intrinsic interest in the company producing superior data assets. In addition, finance functions have the right mindset to analyze and understand data holistically. Bank CFOs can take a lesson from other industries by strengthening their alliance with the CDO and by offering support to, participation in, or co-steering of data initiatives run in individual business lines. That will bring to bear their bank-wide perspective. An integrated approach to data will be crucial to providing business planning and steering, optimizing operations, tapping more cross-selling opportunities, and giving customers fast, convenient access to useful insights in their own accounts.