Taking a cue from the RBI’s views shared in a recent ‘Annual Statistical Conference’, it is time for banks to introspect about the internal data augmentation strategies and linking them with policy and process reforms.

Improved operational efficiency brought about by data analytics can eventually improve stakeholder value.

The kind of secondary data disclosed by banks does not reflect the granularity that is now possible with data analytic tools. The banking sector is yet to fully galvanise internal data analytics for business process re-engineering and for fine-tuning turnaround time (TAT) of various transactions and services.

Similarly, the data dynamics are not fully explored to identify the gaps in response systems to meet customer needs.

Multiple uses of data: The plough back of granular data for improving internal policies, product re-engineering, improved processes and perpetuating a culture of internal sharing of information is yet to take shape. The information stored in data warehouses of banks can be a source to identify systemic weaknesses.

While some banks are smart to make use of internal data to design add-on offerings, feature upgradations, digital marketing and customer connect, the rest are yet to tap the data for such reforms. The decade-long data stored by banks can provide valuable information to remove hurdles and create pathway for scaling up efficiencies.

Transformative data pools: Among many useful data pools evolving from time to time, the banking industry has witnessed multiple reformative uses of the Central Repository of Information on Large Credit (CRILC), and the Special Mention Account (SMA) introduced by the RBI. The SMA data has now become an effective early warning sign to better manage asset quality. By the extensive use of these data pools, many banks can improve their internal competencies and systemic controls to better manage assets but its scope is not fully explored. Therefore, proper collection of data, classification, retrieval, analysis can be used to remove hitches in working and can bring about a culture of information sharing.

Wider exploration of data: But it has to be borne in mind that data collection, storage and its protection call for huge investments. Unless the data is put to optimum use, the return on investment on data management cannot be ensured.

Banks have far greater scope to reorganise and enlarge their data collection templates keeping the business intelligence and operational efficiency in view going beyond the regulatory needs. More important is to institutionalise a regular reflection into the data analytics framework to identify gaps in efficiencies that could be corrected in time.

They can develop an internal information utility toolkit to work as a template for reforms. The details of work flows, time taken for completing various activities, decisions taken and results achieved should be measured. The benchmarking for each set of activity will help in measuring deviations and finding missing gaps.

Going beyond fixing turnaround time for customer-facing activities, data can help in identifying leakage of time measured as a resource so that corrective action can be taken. The reflective analysis of activities will help in identifying impediments in adhering to internal time lines. Data analytics can measure the speed of key decisions and can set better standards and response system to fine tune internal decision-making process.

Based upon these detailed data points, better standard operating procedures (SOPs) can be designed, and training and skill building planned.

Thus, banks should work out innovative data augmentation and analytics strategies to create better value to stakeholders.

The writer is Adjunct Professor, Institute of Insurance and Risk Management – IIRM. Views expressed are personal

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