Banking upon Business Intelligence in Banking

Banking upon Business Intelligence in Banking

 *V V Narendra Kumar

K.Ravi/K.Satish/Masood

Abstract

 t is Business Intelligence (BI)?

Business Intelligence (BI) is a term coined for technologies and applications employed in data collection, access, analysis and information about an organisation’s business. It refers to the use of several financial / non-financial metrics / key performance indicators to assess the present state of business and to assist in deciding future course of action. It is ‘actionable intelligence’.

 

Business Intelligence tools are being used by banks for historical analysis, performance budgeting, business, performance analytics, employee performance, measurement, executive dashboards, marketing and sales automation, product, innovation, customer profitability, regulatory compliance and risk management. Let us take a look at some of these applications

 1. A Bank Environment

  

 

2. Uses of BI in banks

Historical Analysis (time-series)

Banks analyze their historical performance over time to be able to plan for the future. The key performance indicators include deposits, credit, profit, income, expenses; number of accounts, branches, employees etc. Absolute figures and growth rates (both in absolute and percentage terms) are required for this analysis. In addition to time dimension, which requires a granularity of years, half year, quarter, month and week; other critical dimensions are those of control structure (zones, regions, branches), geography (countries, states, districts, towns), area ( rural, semi-urban, urban, metro), and products (time, savings, current, loan, overdrafts, cash credit). Income could be broken down in interest, treasury, and other income; while various break-ups for expenses are also possible. Other possible dimensions are customer types or segments.

 

Derived indicators such as profitability, business per employee, product profitability etc are also evaluated over time.

 

The existence of a number of business critical dimensions over which the same transaction data could be analysed, makes this a fit case for multi-dimensional databases (hyper cube or ‘the cube’).

 

Though it is a major requirement, it hardly receives the attention of BI vendors. For sometime, these requirements were bundled as Executive Information Systems (EIS). But the safe, quantifiable world of computers runs up against a wall of unquantifiable abstractions, value judgments and opinions when designing an EIS system. For one, no two executives are alike. And how information is analyzed, interpreted and acted upon is a very subjective exercise. No surprise, therefore, that BI vendor shifted their focus to terra firma of customer relationship management (CRM) which continues to be the centre of their sales pitch to banks today. Even risk management comes a close second. 

 

Performance Budgeting

Indian banks adopted performance budgeting as a management tool in the sixties. The success of the tool depended on historical data on which the current performance levels could be realistically based, and periodic reviews to take corrective actions if there were large variances between budgeted and actual figures. Historical analysis and performance budgeting used roughly the same indicators and the same dimensions, except for resource allocation to achieve the budgeted targets.

 

Customer Relationship Management (CRM)

As stated earlier, this application is at the centre stage of BI in banking. It is difficult to assess whether it is driven by technology or business. Traditional or conservative banking business models of Indian banking industry relied heavily on personal relationships that the bankers of yesteryears had with their customers. To that extent, ‘relationship’ in the present version of CRM is a misnomer. Let us look into the application of CRM in banking, a little more closely.

 

CRM is an industry term for the set of methodologies and tools that help an enterprise manage customer relationships in an organized way. It includes all business processes in sales, marketing, and service that touch the customer. With CRM software tools, a bank might build a database about its customers that describes relationships in sufficient detail so that management, salespeople, people providing service, and even the customer can access information, match customer needs with product plans and offerings, remind customers of service requirements, check payment histories, and so on.

 

A CRM implementation consists of the following steps:

 

ü      Find customers

ü      Get to know them

ü      Communicate with them

ü      Ensure they get what they want (not what the bank offers)

ü      Retain them regardless of profitability

ü      Make them profitable through cross-sell and up-sell

ü      Covert them into influencers

ü      Strive continuously to increase their lifetime value for the bank

 

The most crucial and also the most daunting task before banks is to create an enterprise wide repository with ‘clean’ data of the existing customers. It is well established that the cost of acquiring a new customer is far greater than that involved in retaining an existing one. Shifting the focus of the information from accounts tied to a branch, to unique customer identities requires a massive one-time effort. The task involves creating a unique customer identification number and removing the duplicates across products and branches. Technology can help here but only in a limited way.

 

The transition from a product-oriented business model to a customer-oriented one is not an easy task for the banking industry. It is true of all the banks, Indian or otherwise. It is also true of all Indian banks; private, public, or foreign; and of whatever generation.

 

A few instances are worth mention here. Head of retail business of a technology savvy new generation private sector bank admits on conditions of anonymity, that there is no 360 degree view of a customer available in his bank. It treats credit card applications from its existing customers in the same way as it does for new customers. A retail loan application does not take into account the existing relationship of the customer with the bank, his credit history in respect of earlier loans or deposit account relationship. And this bank is one of the pioneers in setting up a data warehouse, and a world class CRM solution.

 

Most CRM solutions in Indian banks are, in reality, sales automation solutions. New customer acquisition takes priority over retention. That leads to the hypothesis that

it is BI vendors that are driving CRM models in banks rather than banks themselves.

 

Product silos have moved from manual ledgers to digital records. There is not a

single implemented model of ‘relationship’ in Indian banking industry as of today. 

 

Risk Management

Theoretically, banks transform, distribute and trade financial risks in their role of a financial intermediary. However, the risk management discipline as it is known today has its roots in statistical techniques, which require historical data, both internal and external. Statistical models for measurement of various risks such as credit, market, and interest rate depend on the availability, accuracy and amount of historical data for their predictive power.

 

Though most of this data gets generated out of banking transactions, it needs to be extracted, cleansed and transformed before it can be used in risk measurement models. Most of the risk management in Indian banking industry is regulator-driven.

 

Regulatory compliance

Regulatory compliance requirements in the banking industry worldwide are on the increase. Basel II, anti-money laundering, Sarbanes-Oxley, and Sebi clause 49 are a few examples. All these regulatory requirements share one common feature – they are data-intensive. Some of these requirements are now quite stringent about the quality of reporting, making the chief executive officer (CEO) and the chief information officer (CIO) personally liable for the correctness of reports.

 

Regulatory reporting, therefore, requires a properly-audited data collection and collation process.

 

However, all these BI applications cater to the needs of the top management in banks. But, line managers have a different set of BI requirements, which differ from those of the top management. These requirements constitute ‘Operational BI’.

 

3. Operational Business Intelligence

 

Operational BI embeds analytical processes within the operational business structure to support near real-time decision making and collaboration. This characteristic fundamentally changes the way how data is used, where it exists and how it is accessed. Observes Wayne Eckerson, director of TDWI Research: ‘Operational BI merges analytical and operational processes into a unified whole’. This change is rapidly exposing the limitations of traditional analytical tools.

 

Operational BI helps businesses make more informed decisions and take more effective action in their daily business operations. It can be valuable in many areas of the business, including reducing fraud, decreasing loan processing times, and optimizing pricing.

 

3.1 Key characteristics

ü      Caters to middle management and frontline

ü      Just-in-time delivery

ü      Uses recent transaction data

ü      Less aggregation, more granularity

ü      Embedded processes into business

ü      Handles disparate sources and unstructured data

ü      Availability is a concern

ü      Requires different architecture

 

 

 

 

 

 

4. Operational Business Intelligence in Indian Banks

Analysis,

External data

collection

 

Corporate

Office

 

Regulators

Aggregations, Analysis and Marketing

Mid Tier Controlling Offices

Branches

Since all the transaction data was created at the branch level, it was aggregated, transformed, (and sometimes fudged!) by the branch to create reports for regional or zonal controlling offices. These reports were aggregated for onward submission to corporate office, and were also used to monitor performance of the branches by controlling offices. Corporate office used to aggregate the aggregated reports from controlling offices to arrive at the enterprise level data, which was used for regulatory reporting, historical analysis and performance management. Most of the external data was collected only by the corporate office.

 

This unidirectional flow of data from the branches to corporate office via controlling offices was a direct consequence of the transaction data lying distributed at the branches, regardless of the transaction processing system at the branch (manual, partially automated or fully automated).

 

It would be incorrect to assume that there was no operational BI in this architecture. Yes, it was unstructured, often incomplete, and highly person-dependent. In some ways, it was more relationship oriented than quite a few of CRM implementations of today. In a recent interview, MBN Rao, chairman and managing director of Canara Bank related an incident when he had passed an unsigned cheque drawn in favour of Bombay Electric Supply and Transport by a customer of his branch, after due diligence to find that he was a well-known physician in the locality. It was an excellent example of operational BI.

 

 

 

 

4. Business Intelligence in Banking and Financial Services

Retain and expand your client base, improve cross-selling opportunities, and increase customer profitability through a better understanding of behavior, needs, and preferences?
Detect and deter fraudulent activity such as money laundering and identity theft?
Address globalization issues?
Better manage the risk associated with investments, credit and lending, and consumer bankruptcies?
Increase efficiency of core business processes such as call center management, loan processing, and electronic trading?
Comply with industry regulations such as T+1 and the Bank Secrecy Act?
Utilize an industry standard like XBRL for a real-time, accurate, and single version of the truth?

Conclusion:

For banks to prosper in today’s complex business environments, specific information and knowledge about all operational details is required. The bank’s operations and processes have to be recorded and appropriately stored. This historical data has to be accessible for analysis and knowledge extraction.

 

The solution is to create data warehouses and extract knowledge from the data using BI technology. BI can leverage tactical and strategic decision making based on the vast amount of data that is gathered inside bank’s systems. The KBFs in banking are categorised as being customer and risk related. This customer related data and BI is exploited in all possible ways to augment a bank’s sales. The risk data warehouse and BI techniques are the foundation for risk management and regulatory compliances such as Basel II accord. Finally, we presented a review of typical BI techniques and their applications in the banking industry that points to a conclusion that the exploitation of BI in banking is on an upward trend and that more and more data warehousing and business intelligence applications are expected to be implemented in the future at all levels of banking operations.

 

References/Sources:

 

Operational Business Intelligence in Banking- Special Report November 2007-Hari Misra, Editor-in-Chief, Finsight Media
Review Of Business Intelligence Approaches To Key Business Factors In Banking Goran Radonić, Croatian Institute of Technology,  Katarina Ćurko, University of Zagreb
Bonding BI with banking-Network Magazine-May 2006-Munish Mittal
TDWI’s Facets of Business Intelligence – BI Best Practices for real ROI-Sanjay Mehta, CEO, MAIA Intelligence Pvt. Ltd.

* Faculty Informatics, Alluri Institute of Management Sciences.

More Business Intelligence Articles