With the increasing economic globalization and improvements in information technology, large amounts of financial data are being generated and stored. These can be subjected to data mining techniques to discover hidden patterns and obtain predictions for trends in the future and the behavior of the financial markets. This in turn would result in an improved market place responsiveness and awareness leading to reduced costs and increased revenue.
Analytics 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. The managers of the banks may go a step further to find the sequences, episodes and periodicity of the transaction behaviour of their customers which may help them in actually better segmenting, targeting, acquiring, retaining and maintaining a profitable customer base.
Business Intelligence and data mining techniques can also help them in identifying various classes of customers and come up with a class based product and/or pricing approach that may garner better revenue management as well. Analytics can help banks understand and drive decisions related to customer profitability, as well as enable banking institutions to segment customers according to a multitude of variables – demographics, geographies, account history, etc. – In order to create more meaningful and targeted marketing programs.
Furthermore, analytics can help banks improve retention rates by determining the causes and predicting future customer attrition. In addition, banks can apply analytics to historical data to find out which customers are good candidates for cross-selling and up-selling and as a result achieve increase in revenue and wallet share. For most banks analytics are used as the most powerful weapon in the fight against fraud.
Customer Relationship Management
Customer segmentation and profiling is a data mining process that builds customer profiles of different groups from the company’s existing customer database. The information obtained from this process can be used for different purposes, such as understanding business performance, making new marketing initiatives, market segmentation, risk analysis and revising company customer policies. The advantage of data mining is that it can handle large amounts of data and learn inherent structures and patterns in data. It can generate rules and models that are useful in enabling decisions that can be applied to future cases.
In Banking - analytics and data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will behave in a particular way. For example, a score could measure the propensity to respond to a particular insurance or credit card offer or to switch to a competitor’s product. Data mining can be useful in all the three phases of a customer relationship-cycle: customer acquisition, increasing value of the customer and customer retention.
Banks use their credit risk models to classify these respondents in good credit risk and bad credit risk classes. Seeing the huge cost and effort involved in such marketing process, data mining techniques can significantly improve the customer conversion rate by more focused marketing.
Because of high competitions in the finance industry, intelligent business decisions in marketing are more important than ever for better customer targeting, acquisition, retention and customer relationship. Financial institutions are finding it more difficult to locate new previously unsolicited buyers, and as a result they are implementing aggressive marketing programs to acquire new customer from their competitors.
With the advent of big-data analytics 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 also possible to bundle various offers to meet the need of the valued customers. Analytics can also help the banks in customizing the various promotional offers. It is also possible for the banks to find out the problem customers who can be defaulters in the future, from their past payment records and the profile and the data patterns that are available. This can also help the banks in adjusting the relationship with these customers so that the loss in future is kept to its minimum.
Big-data analytics can be of immense help to the banks for better targeting and acquiring new customers, fraud detection in real time adding a lot more value to existing products and services and launching of new product and service bundles.
Managing and measurement of risk is at the core of every financial institution. Today’s major challenge in the banking and insurance world is therefore the implementation of risk management systems in order to identify, measure, and control business exposure. Here credit and market risk present the central challenge, one can observe a major change in the area of how to measure and deal with them, based on the advent of advanced database and data mining technology.( Other types of risk is also available in the banking and finance i.e., liquidity risk, operational risk, or concentration risk. )
Today, integrated measurement of different kinds of risk (i.e., market and credit risk) is moving into focus. These all are based on models representing single financial instruments or risk factors, their behaviour, and their interaction with overall market, making this field highly important topic of research.
Financial Market Risk
For single financial instruments, that is, stock indices, interest rates, or urrencies, market risk measurement is based on models depending on a set of underlying risk factor, such as interest rates, stock indices, or economic development. Point of interest in a functional form between instrument price or risk and underlying risk factors as well as in functional dependency of the risk factors itself. Today different market risk measurement approaches exist. All of them rely on models representing single instrument, their behaviour and interaction with overall market. Many of this can only be built by using various analytical techniques.
Risk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected return or price of a financial instrument. With the data mining and optimization techniques investors are able to allocate capital across trading activities to maximize profit or minimize risk.
With analytical techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset prices or returns and the risk involved. With this functionality what-if simulations of varying market conditions can be run to assess impact on the value and/or risk associated with portfolio. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub-portfolio can be benchmarked against common international benchmarks.
For the last few years a major topic of research has been the building of quantitative trading tools using data mining methods based on past data as input to predict short term movements of important currencies, interest rates, or equities. The goal of this technique is to spot times when markets are cheap or expensive by identifying the factor that are important in determining market returns. The trading system examines the relationship between relevant information and piece of financial assets, and gives you buy or sell recommendations when they suspect an under- or over-evaluation.