In today’s world, the banking industry is faced with the same challenges as other industries. The economic landscape is diverse, unpredictable and poses several threats. Customer retention continues to be a challenge due to increasing mergers, acquisitions and partnerships googletest. Using Business Intelligence solutions is now even more critical for decision makers.

• Chaotic use of spreadsheets and unmanageable processes

• Customer data from multiple data sources is not standardized. Banks historically have had separate databases for different areas: one for mortgages, another for auto loans, another for checking accounts and so on. But to obtain a full picture of customer relationships, an organization needs access to all customer data enterprise wide.

• Customer Acquisition & Retention: Address economic slowdown and intense competition by retaining market share and adjusting to new demographics- suchas larger senior population and migratory consumers. Address new opportunities in emerging economies (such as the new mass affluent, small businesses, and the unbanked).

• Expand Share of Wallet in Customer Portfolio:
  Banks need to increase emphasis on revenue generation goals through nimbler product development and innovation and expansion into new geographies by using improved and actionable customer insight.

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Our Value Proposition: We implement in 1/4 the Time, incur 1/2 the Cost, provide 2x the Value. We deliver a highly interactive financial metric dashboard which calculates key metrics and ratios in addition to allowing analysis through a large number of dimensions.

Perform market growth and penetration analysis including new combinations of existing business unit data from disparate systems. This solution lets product managers assess product performance daily. It monitors sales activities / interactions with customers – who visited the customer last time? What was discussed? How did the customer behavior develop over time?. You can also assess customer data to analyze customer portfolio across lines of business to identify cross-selling opportunities; Conduct ad-hoc analysis on key client information such as portfolio distribution and performance

• Some KPIs we use to gauge performance:

• Deposit Mix & Average Monthly Deposits by Branch

• Loan Mix & Average Monthly Loans by Branch

• Past Due Loans as a % of Total Loans

• Risk by industry

• Liquidity Ratio

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INVESTMENT ANALYTICS DASHBOARD
For banks with trust or investment accounts, we have developed an investment analytics application which provides an interactive snapshot of the client account holdings and transactions. Gain knowledge of the Fund or Account manager performance by viewing performance returns using AIMR recommended methods. Monthly, quarterly and yearly performance and trends can be quickly generated at the click of a button and a client-friendly Quarterly or Monthly performance report can be produced with ease.

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Data Mining in banking
Data mining can contribute to solving business problems in banking and finance by finding patterns, causalities, and correlations in business information and market prices that are not immediately apparent to managers because the volume data is too large or is generated too quickly to screen by experts. With the advent of data mining and business intelligence tools it has become possible for banks to strengthen their customer acquisition by direct marketing and establish multi-channel contacts, to improve customer development by cross selling and up selling of products, and to increase customer retention by behaviour management. It is possible for the banks to use the data available to retain its best customers and to identify opportunities to sell them additional services.

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Challenges:  In recent times, there has been a lot of consolidation in the banking industry. It has been a challenge for the parent bank to integrate all the information about a customer and have readily accessible knowledge of all the interactions, services and offerings utilized by that customer. Banks are moving towards a "targeted-marketing" approach which can give their customers assurance that their bank "knows" them, and can also help cross-sell appropriate products to the customer.

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Database Mining Lab  can mine your database to develop profitability profiles of customers based on multiple factors such as the amount of money in particular accounts, demographic information, the number of monthly transactions and their choice of banking channels (teller, ATM, online or phone).

• Reduce the number of delinquent and defaulted car loans by correlating the incidence of bank loans with specific loan-applicant characteristics

• Assess retail loan risk by scoring loans using over 100 parameters some of them being payment histories, client ability to pay, debt-ratio, payment-to-income ratio

• Assess performance of commercial loans by different industry segments and subgroups (for example – real estate segment which is made up of apartments, resorts, shopping centers etc)

• Segment customers into distinguishable clusters based on client behaviors related to balance information (maximum and minimum), number of debits, teller transactions, customer profitability etc. Integrate demographic datasets purchased from an external source (like Equifax or Axciom)

• Realize significant cost savings by optimizing non-revenue producing reserve balances. Perform accurate loss-forecasting. All banks are required to keep reserves against loans expected to go bad. If the forecasts are not accurate, monetary fines and insolvency could result. Analyze which part of the portfolio contains bad debt, quantify the bad debt, and use that information to predict losses and readjust reserves.

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