Article: Five strategies to embed analytics in your bank’s DNA

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Five strategies to embed analytics in your bank’s DNA

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Here are five useful strategies suggested by McKinsey and Financial Brand to embed analytics in the banking DNA.
Five strategies to embed analytics in your bank’s DNA

The value proposition of data analytics dovetails with some of the most important priorities of Indian banks today. From cutting down non-performing assets (NPAs), reducing account dormancy and profiling profitable customers to driving targeted customer acquisition and increasing business from the newly banked, analytics makes a critical difference to key banking processes. According to PwC’s Retail Banking 2020 report, 57 percent of global bank executives consider advanced analytics very important to drive actionable insights from their library of data. In addition, 92 percent of the global banking executives consider advanced analytics very important or somewhat important to excellence at work.  Most major Indian banks have now adopted analytics at some level in their key processes. At the same time, banks face challenges in scaling their analytics deployment. 

Here are five useful strategies suggested by McKinsey and Financial Brand to embed analytics in the banking DNA:

Enable a top-down approach to build an analytics culture:

Successful deployment of analytics requires a holistic approach. To embed analytics in the DNA of your bank, it is not enough to build an analytics team or to enable specific people with analytical tools. The analytics program should have the full backing of the CEO and the top leadership, and the senior management should ensure consistent communication that analytics is critical to the bank’s survival. Essentially, the top leadership should dedicate themselves to creating and driving an analytics culture in the organization. 

Drive adoption, not just tools deployment:

It is essential that employees trust the tools that they are expected to work with and overcome their resistance to change. Effective change management is, therefore, the key.  This includes making the value proposition of the change clear to everyone involved at an individual level and effective role modeling to instill ideal behaviors, before adoption. Equally important is investing in skill development through internal and external training, so that your team can succeed with their analytical tools. Finally, ensure that successful adoption is matched with incentives within the framework of your organization’s rewards mechanism.

Build an analytics center of excellence:

An analytics COE works as the backbone of your efforts to drive excellence through analytics. Effectively competing in today’s digital world demands the breadth of data analytics skills: data science, architecture, design, and engineering. Appoint a chief data officer (CDO) to act as the lynchpin of this CoE by driving the analytics strategy as well as the integration with business units. The CDO also defines roles and responsibilities for data management, tracks data quality, and ensures regulatory compliance. The CoE drives the optimization of the data management processes with clearly defined policies on accountability, and the management of data quality, metadata, data lifecycle, and control. In addition to establishing control as early as possible in the data lifecycle, the CoE helps business capture the right data for optimal analytics.

Start small with an analytics discovery process:

The discovery process should start with key high-value questions, rather than data collection. Asking what problems you want to solve with a data solution and estimating the value that the solution can create is an essential first step. This helps you focus on generating actionable insights. Build use cases around your high-value questions and make a priority list of use cases. Then, start small with one use case and scale gradually as you build competencies.

Invest in a robust talent pipeline:

Key decision makers should be trained to use big data and gain actionable insights from it. The training must be specific to each role to help people understand the value of what they are doing and build an organization-wide analytics culture. It is essential that the management speaks to data scientists with confidence to develop this culture. Banks can start with small teams of data scientists and work with skill development partners to build the necessary skills and competencies to scale up gradually.

Higher levels of analytics maturity drive competitiveness:

Indian private banks such as Kotak Mahindra Bank, YES Bank, HDFC Bank, Axis Bank, and ICICI Bank deploy analytics extensively, while SBI is the most analytics-savvy public sector bank, according to a report by Analytics India Magazine. It’s time for banks to leverage the full potential of analytics by developing competencies in advanced and predictive analytics. For example, HDFC Bank has achieved a significant level of analytics maturity with 80 percent of all predictive analytics solutions developed today being based on advanced analytics algorithms. In addition, 95 percent of all marketing activities and efforts at the bank are analytics-driven. For several years now, SBI has combined a fully functional data warehouse and a robust business intelligence platform with highly skilled statisticians to enable analytics-driven processes at every level of the enterprise. To deliver real-time analytics to its executives, SBI is going beyond data warehousing to create a data lake. By deploying a data lake like SBI and 

Kotak Mahindra Bank, banks can create a single version of the truth about the customer and process and analyze data in real time for an instant, personalized decisions using unstructured data and machine learning algorithms.

Advanced analytics helps banks create an enhanced and connected customer experience and enhance their credit, risk, and pricing models. Using social media data, banks can enable a higher quality of risk management and customer service. In these times of financial distress, banks can create a much more sophisticated view of their cost structures and key cost structure drivers using advanced analytics. Banks that have reached a higher level of analytics maturity have invested not only in technology but also in graduate programs or partnerships with external edtech partners to boost and update their analytics capabilities. For example, SBI worked with a partner to create an entirely new brand for digital customers by selecting, training, and onboarding internal resources in digital skills including analytics. It is clear that such banks are able to differentiate themselves and deliver an attractive value proposition to their customers even in uber-competitive markets.

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