How Remote DBA Experts Are Using Hadoop to Make a Difference to the Financial Sector

Take any industry vertical that is leveraging the power of big data analytics and you can be sure that you will find Hadoop hard at work in making a vital difference to the way enterprises can able to store, access and manipulate the data in a seamless manner across platforms and data types. The versatility of Hadoop enables businesses to analyze information faster and gain vital competitive advantages. While Hadoop is being successfully used in virtually every industry one can think of, nowhere is its presence more evident than in the financial services sector. Some ways in which Hadoop is being used:

Detection of Fraud   

Among the biggest challenges to the viability of a financial services enterprise is its ability to prevent fraud, crime, and breaches of data confidentiality. These incidents are not only extremely costly in terms of monetary loss but more importantly exposes the business to the risk of litigation and erosion of its customer base and reputation. The deployment of Hadoop analytics help organizations operating in the financial sector to prevent and detect fraud, both external and internal and also substantially reduce the cost of the exercise.

Hadoop enables organizations to analyze transaction and authorizations at the various points of sale and other data points so that fraud can be easily identified and mitigated before assuming significant dimensions. For example, credit card misuse can be detected by big data analytics by identifying behavior patterns that seem to be unusual for the customer in question. The card can be temporarily suspended till the account owner can confirm that it is not been lost or stolen.

Management of Risk

The success of financial enterprises depends essentially on how best it can assess risk because risk perception is a major component of product/service pricing and profitability. Hadoop deployment enables organizations to evaluate credit exposure as well as analyze transactions to determine risk according to market behavior simulations. The results are used to allocate scores to potential clients as well as existing customers. Hadoop solutions enable financial businesses to undertake a holistic view of the risk and its possible impact enabling them to make the most informed decisions.

Optimization of Efficiency of Contact Centers

Keeping customers fully satisfied is at the core of the success of any financial services company because money and wealth are always extremely sensitive subjects. Hadoop is playing a very critical role in many organizations in resolving problems even before they arise by being able to anticipate customer requirements. Analysis of data emanating from the contact centers can be used to provide agents and customer service personnel with insights that are concise and timely for better customer satisfaction and that too in a manner that is more cost-efficient and can improve the success rate of cross-selling.

Offer Optimization with Customer Segmentation

The deployment of Hadoop gives financial organizations a better way of understanding the needs of customers at a really micro level. This enables them to devise and target specific offers to customers with profiles that are likely to get the best conversions. The conversion rate is typically far superior because big data analysis allows profiles to be studied in great detail with regard to demographics as well as past behavior so that offers can be customized to the greatest extent. As a result of this, customers are more satisfied, and display greater loyalty to the business and allow for increased profitability by minimization of customer acquisition costs and attrition.

Analysis of Customer Churn

It is a proven fact that it is always cheaper to retain existing customers than to acquire new ones. This all the more important in the financial service industry due to the sensitivities that are involved. Even customers like to transact with the same company as their financial profiles are known to the company and they do not have to engage in repetitive document submission and evaluation that would be required if companies were to switched. Using the advanced big data processing capabilities of Hadoop companies can analyze customer behavior and find out why customers have become disgruntled and abandoned them. Repetitive analysis of such cases provides insights into issues that are leading customers to leave for the competition.