It is not an exaggeration to say that modern time is the era of digital information. The amount of data of various nature is growing exponentially every year in the last decade. Internet, mobile devices, cloud technologies, and computers can provide access to large data streams and repositories.
We both consume and produce volumes of data, which does not disappear without a trace. It is stored, classified, structured and used for many purposes. But the explosive nature of data growth, information heterogeneous nature and difficulties in classification cause problems as well. Powerful tools and assistants are needed to deal with large data volumes.
That is why the phenomenon of Big Data was distinguished by scientists. They define Big Data as large either structured or unstructured data sets of a wide variety, coming from different sources.
The data is so large, complex or fast that it’s difficult or even impossible to process it with the help of traditional methods. FICO Big Data Analyzer automates it, and makes it simple in use despite the complexity of Hadoop code that is used for Big Data processing.
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Financial data and analytics: searching pearls in the mud
The explosive growth of data volumes made banking businesses think of the necessity in Big Data financial analytics. And now, Big Data technologies are the main point of interest for financial institutions. On the one hand, they give banks the opportunity to offer customers better services. On the other hand, Big Data technologies help to improve banking businesses and their security.
Modern financial data analysis tools process constantly increasing amounts of data solving three major tasks:
- Сollect and process large data sets than traditional financial data analysis software does.
- Deal with quickly incoming large data volumes that are constantly growing.
- Work with structured and unstructured data simultaneously and in different aspects.
Today, more and more financial institutions realize the importance of data. They clearly understand that Big Data technologies are a new locomotive that gives a powerful thrust to the financial industry in general. The majority of leading banking businesses prioritize Big Data technologies and analytics in their activity.
In the financial sphere Big Data is applied in three main fields:
- sales increase and customer’s loyalty improvement;
- anti-fraud;
- credit scoring.
Increase sales and improve customer’s loyalty
The amount of financial products sold to a customer is a significant criterion of efficiency of the bank’s work. To maximize this figure, it is necessary to offer a service at the appropriate moment, when a customer really needs it. But how can banks know customers’ needs?
Big Data helps with it. Data mining technology finds customers’ personal preferences and needs analyzing their search history, social network accounts, internet purchases, and credit score. As a result, certain products that can suit customers are selected and offered. That, in turn, influences customers’ loyalty.
Anti-fraud
Anti-fraud systems analyze a lot of parameters to detect and prevent fraudulent activities. Big Data creates an average customer profile. The more data about typical customer behavior, the more secure the anti-fraud system is.
For instance, users who do not leave digital tracks are suspicious. Even a recently created email may become an alarming signal for the security system as well as a fake account in social networks.
Credit scoring
The third field is potential debtor analysis or credit scoring. Nowadays, banks take into consideration not only traditional data about their clients, i.e. social-demographic characteristics, credit history, income level, etc. History of purchases is taken into account too. Banks also analyze customers’ behavior in social networks, make conclusions about his social status, level of education and qualification.
For corporate and business crediting, banks study the tone and frequency of potential debtors mentioning in mass media.
Big Data financial services: functions without malfunctions
The Big Data financial industry is booming these days. More and more sophisticated and intelligent software for Big Data Analysis and Data mining is developed. But functions Big Data technologies perform are still the same.
- Customers segmentation. Financial institutions put their priority on the collection of information about customers. The thorough analysis provides the opportunity to divide clients into groups according to certain characteristics. Due to client segmentation, Fintech companies can easily verify and adapt their products to satisfy customers’ needs within all segments. It also helps to find customers who spend more money on a regular basis. In addition, the personalization of services leads to better clients’ satisfaction.
- Fraud detection. Big Data technologies give advantage of fraud detection and prevention. It is evident that the growth of online transactions and the development of Internet banking make clients more vulnerable to scams and fraud. Big Data tools help financial institutions to draw customers’ behavior patterns. When unusual activity is detected, the system considers it suspicious and get in touch with an account owner.
- Risk management. Risk pursues us everywhere. The problem of risk reduction is especially actual in business to avoid financial losses. Big Data technologies in the financial industry give a powerful advantage of risk identification in the early stages. Estimation of potential risks and minimizing their effect is the main aim of financial data analysis tools.
- Personalization of services. More and more customers are looking for flexible fintech services nowadays. Services personalization is a marketing tool that helps attract clients. Big Data software creates personalized products’ offers and more convenient service infrastructure for customers. The offers either help find more suitable services or save clients’ money.
- Company’s inner rules supervision improvement. Each financial company has its rules to abide by. Regular auditing and supervision of security and confidentiality of financial information are vital. Big Data tools provide the opportunity to automate the process and make it more effective.
It is hard to underestimate the role of Big Data technologies in the financial industry. All the advantages they give are aimed at the improvement of B2B and B2C processes.
FICO Big Data Analyzer: the gold miner of data deposits
FICO Big Data Analyzer is a tool for business users, data scientists, and analysts who manage Hadoop based types of data. The major advantage of the application is that it hides Hadoop’s complexity.
User-friendly interface, simple set-up system and excellent compatibility are offered instead. Users gain more understanding about the system itself, can apply it more effectively, and consequently get more benefits from it, increasing business efficiency.
FICO Big Data Analyzer helps to extract the use of data analysis from behind IT-walls and to make it accessible and understandable for business users and analysts. The program is an end-to-end analytic modeling lifecycle solution.
Big Data Analyzer is useful throughout all stages of data processing from extracting and exploring data to creating predictive models and using the data for decision making and discovering business insights.
Combined with FICO Analytic Modeler and FICO Decision Management Platform, the application reveals its full potential making Big Data readily accessible and enabling business analysts and data scientists to solve relevant business problems more quickly and effectively.
Conclusion
The complexity of Big Data structure and its processing methods shouldn’t hamper businesses from the advantages of using them. On the contrary, the variety of data sets and sources they come from should inspire businesspeople to be more customer-focused and flexible.
FICO Big Data Analyzer makes the Data mining process easy extracting the simplicity of business decision making out of the complexity of data streams.
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