BUSINESS INTELLIGENCE IN BANKING

Business intelligence (BI) is a computer basedtechnique used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.

"Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. Business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others.

BI technologies provide historical, current, and futuristic views of business operations. The common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors.

BI applications in an enterprise:

Business Intelligence can be applied to the following business purposes, in order to drive business value

  • Measurement – program that creates a hierarchy of Performance metrics and Benchmarking that informs top management about progress towards business goals
  • Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform Business Knowledge Discovery. Frequently involves: data mining, statistical analysis, Predictive analytics, Predictive modeling, Business process modeling
  • Reporting/Enterprise Reporting – program that builds infrastructure for Strategic Reporting to serve the Strategic management of a business, NOT Operational Reporting. Frequently involves: Data visualization, Executive information system, OLAP
  • Collaboration/Collaboration platform – program that gets different areas (both inside and outside the business) to work together through Data sharing and Electronic Data Interchange.
  • Knowledge Management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge Management leads to Learning Management and Regulatory compliance/Compliance

BI in banking:

BI in banking evolved through Manual Systems to management Informationsystems with Computerization. Banks had efficient transaction recording systems before computerization also. The manual systems too had effectively provided the necessary reports for management and regulatory requirements.These reports weremanually consolidated at lower offices and final reports were presented at head office level. These manual systems worked well till the scale of operations of the banks were small.

As the banks grew in size and expanded geographically the number of branch network grew leaps and bounds and so the, the volume of transactionsbecame quite large and manual operationsbecame time consuming and errorprone. To cater the load of operations from all bank branches spread across geographies the banks have started using computers and slowly banks have become fully automated.

The manual management information system (MIS) in the banks had thefollowing drawbacks:

  • The data is laying in different silos
  • There was a Time lag in data collating.
  • Data quality is poor.
  • Unavailability of customer specific data
  • Data granularity required for developing analytics (what if scenario, drill down)
  • Was not available to decision makers.
  • Reporting activity competed with business activity for resources at the branch.
  • Data classification rules were not applied uniformly across the organization,and also varied with time.

Slowly,majority of the banks began using information technology for MIS. Theinflexibility of Cobol programmes and batch processing was soon overcome bypowerful desktop systems with rudimentary database systems, which allowed banksto analyse data, once it has been received in manual form from branches,the same was transcribed into machine readable formats and validated. Quite a few of regulatoryreports were also produced in thisway. These earlier initiatives laid the foundations of BI in banking.

Uses of BI in banking:

Business Intelligence tools can be used by banks for historical analysis,performance budgeting, business performance analytics, employee performancemeasurement, executive dashboards, marketing and sales automation, productinnovation, customer profitability, regulatory compliance and risk management.

Examples of these applications are;

Historical Analysis (time-series)

Banks can analyze their historical performance over time to be able to plan for thefuture. The key performance indicators include deposits, credit, profit, income,expenses; number of accounts, branches, employees etc. Absolute figures andgrowth 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, andother income; while various break-ups for expenses are also possible. Otherpossible dimensions are customer types or segments.Derived indicators such as profitability, business per employee, product profitabilityetc are also evaluated over time.The existence of a number of business critical dimensions over which the sametransaction data could be analyzed, makes this a fit case for multi-dimensionaldatabases (hyper cube or ‘the cube’).

Analyzing,interpreting and acting upon on the information is a subjective exercise. Hence, theBI vendor shifted their focus to customer relationship management(CRM). CRM continues to be the centre of the attraction to banks today and riskmanagement comes to second.

Customer Relationship Management (CRM):

CRM is at the centre stage of BI in banking. However, it is becoming difficultto assess whether it is driven by technology or business. Traditional or conservativebanking business models of Indian banking industry relied heavily on personalrelationships that the bankers of yesteryears had with their customers. If we look intothe application of CRM in banking, more closely,CRM is an industry term for the set of methodologies and tools that help anenterprise manage customer relationships in an organized way. It includes allbusiness processes in sales, marketing, and service that touch the customer. WithCRM software tools, a bank can build a database about its customers thatdescribes relationships with sufficient detail so that management, salespeople, service people, and even the customers can access information, match customersneeds with product plans and offerings, remind customers of service requirements,check payment histories, and so on.

A CRM helps a bank with the following:

  • Find customers
  • Get to know them
  • Communicate with them
  • Ensure they get what they want (not what the bank offers)
  • Retain them regardless of profitability
  • Make them profitable through cross-sell and up-sell
  • Covert them into influencers
  • Strive continuously to increase their lifetime value for the bank.

The most crucial and daunting task before banks is to create anenterprise wide repository with ‘clean’ data of the existing customers. It is wellestablished that the cost of acquiring a new customer is far greater thanin retaining an existing one. Shifting the focus of the information fromaccounts tied to a branch, to unique customer identities requires a massive onetimeeffort. The task involves creating a unique customer identification number andremoving the duplicates across products and branches. Technology can help herebut only in a limited way.

The transition from a product-oriented business model to a customer-oriented oneis not an easy task for the banking industry. This is true in case of all the banks of all the banks, Indian orotherwise.

For example, even today, in a tech savvy new generation private sector bank there is no 360 degree view of a customerdetails. They treat the same way afor a credit cardapplications to its existing customers as well the new ones.

A retail loan application does not take into account the existingrelationship of the customer with the bank, his credit history in respect of earlierloans or deposit account relationship. And the private banks are the pioneers insetting up a data warehouse, and a world class CRM solution.

Most CRM solutions in Indian banks are, in reality, sales automation solutions. Newcustomer acquisition takes priority over retention. That leads to the hypothesis thatit is BI vendors that are driving CRM models in banks rather than banks themselves.Product silos have moved from manual ledgers to digital records.An implementation model of ‘relationship’ in Indian banking industry is hard to see as of today.

Most of the BI applications cater to the needs of the top management inbanks. But, line managers have a different set of BI requirements, which differ fromthose of the top management. The line managers of banks require operational business intelligence.

Operational Business Intelligence:

Operational BI embeds analytical processes withthe operational businessstructure to support near real-time decision making and collaboration. Thischaracteristic fundamentally changes the way how data is used, where it exists andhow it is accessed.

Thus ‘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 effective action in their daily business operations. It can be valuable in many areasof the business, including reducing fraud, decreasing loan processing times, andoptimizing pricing.

Characteristics of Operational Business Intelligence:

Caters to middle management and frontline:

Operational BI delivers information and insights to those managers that are involvedin operational or transactional processes. For example while serving a customer over the phone if a customer executive get a flash on his computer screen on the likely requirements of the customer based on his profile and past transaction behavior. This is an example of operational business intelligence.

Just-in-time delivery:

To manage time sensitive process the needed information should be delivered in near real-time i.e. within minutes or hours. Operational BI will help in reducing user reaction for a business issue. The reduced user reaction time with the help of operational BI can bring business benefits to the organization.

For instance, the ability to detect and react more quickly to the fraudulent use of acredit card is a good example of how operational BI can provide business value.

Byanalysing the history of fraudulent situations, the BI system can be used to developbusiness rules that signify potential fraud, and operational BI can be used to applythose rules during daily business operations. The closer to real time the fraud canbe detected; the less is the operational risk.

However, not all operational BI systems need to be near real-time. Reducing actiontimes to close to zero are is beneficial only in specific types of businessrequirements such as the fraud example. In fact, operational BI can be classifiedinto being demand-driven and event-driven, the latter being more automated. If theaction time requirement is a few hours, business users or applications can use theBI system at on-demand analysis and evaluate the results manually to determinewhether any action is required. In the demand-driven case, it is the user who drivesthe BI system.

But if the action time requirement is two seconds, then on-demand will not besuitable. In this scenario BI systems must track business operations continuously

and automatically run analyses to determine whether any action is required. If it is,the business user must be alerted about the situation and sent recommendationson potential courses of action. In case of a fraudulent credit card transaction, the BIsystem is expected to refuse authorisation. In event-driven BI, business operationsand the BI system drive the user. It is obvious that the implementation of eventdrivenoperational BI is more complex than demand-driven BI.

Uses recent transactional data

Data used for operational analysis is frequently accessed before getting loaded intothe data warehouse. The latency in a traditional data warehouse implementationresults from the batch mode in which it is populated. It is more suited for strategicapplications such as historical analysis, risk management, performancemanagement etc. But a dashboard needs to be as close to transaction data astechnically feasible.

Less aggregation, more granularity

In a sharp contrast to traditional BI in which pre-aggregation, with optional drilldown to detail levels is a norm, operational BI normally requires more of datagranularity to address the needs of the specific operational function it supports.Traditional BI aims at a holistic view of corporate performance, while operational BIis process and user specific. Yet, some operational BI requirements do requireaggregated data, such as the lifetime value of a customer, which is required for adirected sales call.

Embedded into business processes

Operational BI is intricately connected to transactional business processes. Theextent of this integration depends on the level of implementation. One could use itto generate operational reports to analyse processes, or monitor them usingdashboards and scorecards. In these two levels there is not much of integration.

Inthe other two levels, where operation BI is embedded into business processes eitherto facilitate them (demand-driven) or to execute other processes (event-driven), it isembedded into the process.

Handles disparate sources and unstructured data

Traditional databases and data warehouses do not take into consideration theincreasing use of unstructured data; such as emails, telephone calls, letters, internalnotes etc, stored outside these systems, which are of critical value in an operationalBI implementation. Another issue that it has to handle arises out of the disparatetransaction systems in use in most of the banks. The variety of banking servicesmakes it very complex and often impractical for a single software solution to handleall kinds of transactions. Extracting data from such disparate systems and makinguse of unstructured data is required to be handled by an operational BI system.

Availability is a concern

The high level of integration with transactional business processes demand thesame level of availability from operational BI implementations that transactionprocessing systems have to provide. An outage of an operational BI applicationcould have a direct impact on the organization’s ability to do business or to serviceits customers. Therefore, availability becomes a critical issue for operational BIapplications.

Requires different architecture

Traditional BI vendors had built their products using proprietary architectures.While these architectures are ideal for strategic BI, they are not suited foroperational BI. Because operational BI entails coupling BI applications with operationapplications and operational processes, a component-based, service-orientedarchitecture (SOA) is necessary to fully support operational BI. Service-orientedarchitecture that lets users access real-time knowledge with a set of service feedscan maximize business agility while reducing complexity. For example, SOA flexiblyand cost-effectively supports the midstream, on-the-fly data collection and analysisnecessary for operational BI. Service orientation also supports operational BIthroughout the business by pushing BI data out to the mobile workforce andenabling workers across the enterprise to incorporate this vital data into theirworkflow. The straight-through processing requirements in the banking industrynecessitate immediate risk analysis, which in turn requires an online BI capability.

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