Data Mining and Financial CRM: Enable Optimal Performance in 3 Vital Areas

Financial CRM is a popular tool for gaining loyal customers and optimizing lead generation. With a motto to understand customer needs and build stronger customer relationships, companies in the financial industry have constantly adopted CRM solutions for sustained progress. When information from data mining is successfully incorporated in the CRM strategy, advisors can offer the most appropriate or profitable investment advice for individual records.

Financial institutes like brokerage or distribution companies offer financial advice and solutions to a variety of clients on a daily basis. A large number of client interactions take place which offer valuable customer data. The copious amounts of customer data generated daily by financial institutes makes the use of data mining techniques a must within this sector. Data mining tools can go through the most complex and varied data to extract pertinent information, these results can be displayed for users across geographies through a CRM software.

Understanding data mining
Data mining is a process which involves technical analysis of existing data to identify patterns and relationships which are then used to predict behavior and possible outcomes. Complex algorithms and models are used to find out previously unknown correlations within the data. The aim of data mining is to focus attention on the most crucial and valid information for customer interactions. This aids informed decision making for improved product offering and customer service.

There are several ways in which data mining may be useful in the financial sector. Here we highlight some of the major areas:

Portfolio Optimization
With the large amount of financial transactions taking place each day, companies are opting for dynamic portfolio optimization enabled through data mining.
  • Determination of optimal portfolio weights based on historical data.
  • Data mining sorts vast historical data and extracts the most crucial information to optimize portfolio performance.
  • Predictive models are created to deduce expected returns from a financial instrument.
  • Data mining is also used to provide different scenarios of possible risks and profitability from the portfolio.
Risk Management
Risk is a major factor in any type of financial planning. There are many types of risks involved in financial markets, including credit risk, investment risk, business risk and debit risk. Detecting such risks in advance using data mining tools is crucial to maintain financial progress and profitability.
  • Sophisticated data mining tools can sort through piles of electronic data and process it to detect financial risk. With the key component of risk identified, firms can take necessary actions to alleviate the risk. 
  • Market risk for individual financial instruments, such as interest rates or currencies, can be analyzed based on data mining models which offer different permutations and combinations of the risk factors. 
  • Financial institutions can also measure credit risk to identify possible bad debts as well as decide on interest rates for loans.
Trading
An important application of data mining within the financial sector is to predict movement of trading instruments like currencies, interest rates or equities.
  • By analyzing historical data, data mining can help identify factors that affect market conditions. 
  • Analysts can predict when market rates will fluctuate and give precise recommendations on when to buy or sell. 
  • As trading involves prediction of only the short-term price changes of a product, data mining tools can help assist these decisions by providing valuable input on whether the product may be undervalued or overvalued. 
  • The job of a financial analyst is to pick up on trends, be it social, economic, political, technological or any other, that may affect the price of financial assets. Data mining can provide a highly analytical and organized approach to keeping up with such trends. By keeping a well-fed record of such trends and their effects, data mining applications can make valuable predictions about the occurrence and impact of such factors in the future.
Access to information generated through data mining tools should be quick, secure and easily accessible to optimize performance and gain a customer’s confidence. When results from data mining are incorporated with an effective financial services CRM solution, the most relevant information relating to a lead or account is displayed for users, thereby optimizing cross selling, support and lead conversions.

From customer profiling to hard core business processes, data mining and financial services CRM tools are crucial for financial services today. As the entire ambit of the sector lies within complex financial analysis and customer orientation, it is not hard to see how valuable the data mining approach can be within the sector. As economies plummet and customers become more aware and critical of their financial assets, analysts and advisors need to up their game to always stay ahead of markets and consistently provide the best advice.



3 comments:

  1. Data mining is helpful in each decision to make it more feasible in less time. It is significant to make the data available in a readily workable format where it can be of great help to your business. Outsourcing of data mining services helps businesses to manage their data effectively. data mining services

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  2. Very informative and impressive post you have written, this is quite interesting and i have went through it completely, an upgraded information is shared, keep sharing such valuable information. automated web data extraction

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