BI in Banking Industry

Presented byShabnam GargPriyanka Goel

Prateek MaheshwariChandraShekar

Ramvikas GunnamYogesh Thakur

Huge volume of data resides on servers spread across the globe.

The volume of data requires the need of specialized tools for analysis of the data.

Better understanding of customers transactions can help in better STP and enhancing profitability

BI can also help organizations in trend analysis, risk management, portfolio management, fraud detection and CRM etc.

BI in BFSI

Trend Analysis- analysis of previous performances of firms and forecast their future earnings.

Risk Management- identifying and managing market risk, credit risk, interest risk.

Portfolio Management-balancing the risk and return on clients portfolio.

CRM- Enhancing Customer Relationship Management.

Fraud Detection- Discovery of customers most probable to default.

Banking Applications

Centre stage of BI in banking.

Driven by technology / business.

Improving the personal relationships.

Set of methodologies and tools for organized relationship management.

How clean the data is and how well can one extract value from it.

CRM

Business processes – sales / marketing / service.

Customer database describing relationships in sufficient detail.

Access information, match customer needs with product plans and offerings.

Transition from a product-oriented business model to a customer-oriented one.

Touch Points

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.

Implementation Steps

360 degree view of a customer.

Existing customer analytics.

Sales automation.

Management & Operational BI.

Clean Data from various collection tools.

Turn data into actionable information and drive improved performance.

Knowledge Gain

Conduct instant analysis on marketing ROI and invest wisely.

Analyze your sales pipeline to find out where you need to focus.

Monitor service and customer satisfaction throughout the service lifecycle.

Ask any question about performance at any time.

Application

Financial Account Management.

Automate Team- and Role-based Processes.

Generate dynamic Executive Reports.

Regulatory Compliance & Security.

Key Benefits

BI tools illustrate patterns that straight analysis alone cannot.

Fifth Third – doesn’t do too well percentagewise with selling cards.

HDFC – Identification of unwanted sectors.

ICICI - All customers are not equal; recognize and reward best customers.

BI Sells Itself

Fraud Detection using Business Intelligence

Techniques

Statistical Techniques Calculation of various statistical parameters Time-series analysis Clustering & Classification

Artificial Intelligence Techniques Expert system Neural networks

Techniques used for fraud detection

Supervised Learning In this method, the given data is compared

with previously available data to check whether the sample is legal or faulty

The previously available data is already labeled as faulty or otherwise

E.g. Bayesian learning neural network is implemented for credit card fraud detection

Types of Learnings

Unsupervised Learning In this method, we do not make use of

labeled records. We rather look for the variables that behave

unusually and create suspicion E.g. Break point analysis applied to

spending behavior in credit card accounts

Types of Learnings

KBC bank is based out of Belgium It focuses on private clients, small and

medium-sized enterprises It implemented SAS data mining tool to

detect internal and external frauds The tool has greatly increased the chances

of detecting frauds

Enhanced fraud detection at KBC Bank

The tool operates on a central data cube containing loan information

It takes into account 14 fraud rules which all the loan accounts should comply with

An example of a rule is whether the pay back account really belongs to the credit owner

The tool gives a complete report of all records deviating from the rule engine

How the tool works?

The e-inspection tool gives a risk score of all the branches based on the 14 rules

Drill-down to a single branch is possible The tool greatly helps the inspection team

to identify the list of branches that need greater attention

Increases the overall profitability

Benefits

Risk measurement approach can be aggravated to quantify the risk of a diversified portfolio.

And along with forecasting models it can provide the expected return or price of an financial asset.

data mining and optimization techniques can help investors to allocate capital across trading activities to maximise profit or minimise risk.

With data mining techniques it is possible to provide extensive scenario analysis.

Various scenario results can be regarded by considering

actual market conditions.

Portfolio Management

Information, Selection &

Optimization

Risk/ReturnEfficientPortfolio

Of Instruments,customer

Return Predictio

n

Risk

Restriction

Option

News

Other Sources

data mining methods based on past data as input to predict short term movements of important currencies, interest rates, or equities.

It identifies relevant market factors and examine it with relevant information to suggest whether the asset is under priced or overpriced.

The number of factors that even an experienced trader can account for are limited and hence many a times predictions fail.

Trading

Economic Factors

Market Factors

Political Factors

InformationSelection

Buy

Neutral

Sell

Data mining techniques are used to discover hidden knowledge, unknown patterns and new rules from large data sets, which maybe useful for a variety of decision making activity.

With the immediacy offered by data mining, latest data can be mined to obtain crucial information at the earliest.

This in turn would result in an improved market place responsiveness and awareness leading to reduced costs and increased revenue.

Systems based on a combination of data mining techniques and artificial intelligence methods like Case Based Reasoning (CBR) and Neural Networks (NN) have enabled to create faster and better prediction.

Trading

Credit and market risk are central challenge Credit risk: Key component in the process of

commercial lending to determine the prospective borrower

Data mining application: Modeling of credit instrument’s value through the default probabilities, rating migrations and recovery rates.

Source: Data mining in banking and finance: A note for bankers, IIM Ahmedabad

Risk Management

To distinguish borrowers who repay loans promptly from those who don't.

To predict when the borrower is at default To determine whether providing loan to a

particular customer will result in bad loans. Behavior and reliability of the customers

towards credit card.

Source: News International. Economist Intelligence Unit

DM application…

The risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events.

Includes legal risk which is the risk of loss resulting from failure to comply with laws as well as prudent ethical standards and contractual obligations.

Reputational risks not included.

Operational Risk

CHAID decision trees: One of the best ways to identify financial profiles of firms and determine operational risk factors.

educational background of managers, status of managers, annual turnover, operating length of firms, expenditure of energy, and quality standards, and usage of credit as operational risk factors for hedging operational risk and raising financial performance.

Source: Financial Profiling for Detecting Operational Risk by Data Mining, Baskent University, Turkey

Operational risk assessment

Early warning to avoid distress Road maps for good credit rating Better business decision making Greater likelihood of achieving business

plan and objectives Loan facilities at favorable conditions and

minimum cost To use resources more effectively

Benefits of risk management

Data mining and optimization provides-

Ability to allocate capital across trading activities

Profit maximization and risk minimization Trade recommendations and portfolio

structuring as per the user requirement. Extensive scenario analysis capabilitiesSource: Data mining in banking and finance: A note for bankers, IIM Ahmadabad

Portfolio Management

Voted as the No. 1 bank in India on the basis of CRM initiatives in 2009.

BI infrastructure includes◦ A Terradata DW based on an Oracle DB and

several flat files.(presently the bank has migrated to Sybase IQ)

◦ ETL was performed by using Informatica power Centre

◦ The front end business tools were provided by SAS(enterprise BI server, enterprise miner and text miner)

BI in ICICI bank

Benefits Customers’s usage pattern Understanding various transactions pertaining to savings accounts, credit

cards, fixed deposits, etc. New product development: Analysis through Behaviour Explorer, whereby customer profiling can be

undertaken by using ad hoc queries, thereby enabling creation of more personalized products.

Central data management: Integration of different divisions including retail banking, bonds, fixed deposits,

retail consumer loans, credit cards, custodial services, online share trading and ATM.

Enhanced Cross sellingMost home loan consumers also selected ICICI bank as their partner for other loans as well.

Increased scalebility The tremendous amount of new data generated every year gets loaded

smoothly on the warehouse

BI in ICICI bank

http://www.icicibank.com/aboutus/pdf/SAS%20-%20ICICI%20Bank-Sep05.pdf

http://www.informatica.com/INFA_Resources/cs_icici_6806.pdf http://www.sas.com/offices/asiapacific/india/success-stories/SA

S-Business-Intelligence-unifies-reporting-at-ICICI-Bank.

http://www.cindiainfoline/fmcg/stma/ Customer Relationship Management (CRM) Best Practices and

Customer Loyalty -AStudy of Indian Retail Banking Sector- http://www.eurojournals.com/ejss_11_1_06.pdf

http://www.usfst.com/article/Banking-on-business-intelligence/ http://www.sas.com Operational business intelligence in banking-journal of the

indian banks asscociation.

References

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