An important task in business intelligence for financial applications is using predictive technologies to identify patterns and trends in structured and unstructured data. They can be used to predict user behavior, perform credit scoring, attract new customers, create additional products, and assess their popularity.
Predictive analytics systems are closely linked to big data and artificial intelligence and based on machine learning tools.
What is it, and how does it look?
There are no ready-to-use off-the-shelf solutions in this domain, so almost any bank or financial institution has its own developer team or outsources the development of new products to companies specializing in this field.
The software (usually powered by self-learning and self-improving neural networks) is developed according to customer requirements. It collects a vast amount of information, processes it, finds interrelations, and adjusts the decision-making strategy and method.
Financial applications
Increasing the efficiency of work with the customer base. At present, everybody is already using the services of banks and insurance companies in one way or another. Therefore, the main ways of increasing revenue are the transfer of customers from one institution to another (market redistribution) or creation of additional products (virtual cards, deposit offers, and much more).
Each bank or insurance company has its customer base used for periodic telemarketing campaigns. Predictive analytics solutions can raise the efficiency of this process: they can choose only the phone numbers, the owners of which are more likely to accept an offer. Frequent telemarketing campaigns are undesirable, so the possibility of distributing offers between target groups strongly raises the probability of success.
Customer credit scoring. This is a relevant task for the banking sector. Based on the analysis of gender, age, completeness of data, and other parameters, the product may decide on the possibility of providing a loan, determine the recommended interest, and the associated risks.
Assessment of risks of an insured event occurrence. The program determines the cost of a particular policy, analyzing the gender, age, profession, frequency of previous requests for insurance payments, as well as other parameters.
Marketing and customer analysis. The solution can define the promising directions of work to create new exciting products and analyze the behavior of clients and their demands. Such a task requires collecting a large amount of data, but the benefits can be huge.
Work with personnel. This item is relevant for any sphere. It allows you to assess the quality of employees' work, identify the bottlenecks for each specialist, as well as to retain valuable staff, offering them timely promotions, reassignments, loyalty programs, or training courses.
Key predictive analytics tools
Azure Machine Learning, SAS, IBM SPSS, Loginom, R, and Python.
All of these products are convenient and offer all the required functions. With some of them, you can also create predictive models, while with others, you can interpret them or do both.
Tools are selected specifically for each case. There are the following parameters:
Support of full data analysis lifecycle. The tool’s efficiency in data mining, building data models, and evaluating their performance.
Knowledge integration and support. The data from the analysis should be integrated with other areas of business. Data collection from a variety of sources has to be supported as well.
Convenient and intuitive interface that is easy to understand for different types of users.
Adaptability and autonomy. Ability to work with minimal interference of developers and technical specialists.
Softline is at your service
Specialists proficient with predictive analytics tools are very rare, and their services are expensive. You can build an entire development department, but it is much better to address an external company that has all the necessary expertise and in-house staff, because:
- You do not need to recruit staff and search for specialists,
- You do not have to pay the salary to employees when they are idle,
- You can rest assured about the end result and timing.
Softline has an in-house development department with competent specialists capable of creating products that solve many tasks for financial applications.
Robots evaluate risks
For the bank, risk assessment is critical. Any risk is a real possibility to lose profit or suffer financial losses. When granting a loan, the bank should be sure in the customer’s solvency. The intelligence system can analyze human behavior in social networks instead of asking a thousand questions.
One of the largest Russian banks has already implemented such a technology. It creates a psychological profile of a person and estimates their trustworthiness, analyzing five character features via social networks: trustworthiness, openness, sociability, law-obedience, and emotional instability. This methodology has already brought the bank $50 M of net profit.
