The financial industry is a highly competitive space. This is facing a new generation of services and adjustments. This is an industry that needs to use big data to ensure personalization, security, and everyday investment decisions.
The financial services industry strives to adopt only reliable technical innovations. Big data technologies have become a powerful tool in understanding market behavior, allowing to offer new directions for the financial industry. The use of big data in financial services goes beyond stock price forecasting. New data types are revolutionizing the entire financial industry.
Reduce the Risks of Bad Loans
Bad or non-performing loans have a negative impact on the bank’s profitability, can lead to credit losses and, at worst, default. Adequate management of bad loans involves banks identifying such loans at an early stage and writing down the value of them equal to the expected credit losses. However, it is much more effective to predict bad loans by analyzing possible risks and customers’ creditworthiness using big data analytics tools before issuing a credit.
The use of big data software will significantly accelerate the analysis of a large amount of data for each customer and helps to shorten the decision-making time. For example, big data helps to collect data to analyze all possible risk factors when issuing loans and credits, taking into account even the smallest indicators, such as the number of friends on social networks or activity on the Internet. This makes the approach to each customer flexible, scalable and as personalized as possible.
The rise of big data analysis carries enormous potential for any type of investment. The financial industry has been successfully applying forecasting algorithms for almost fifty years, but only the last decade has become a real breakthrough in both trading and investing in general. The use of robotic advisers has significantly improved the process of predicting market behavior, allowing to make more informed decisions. Besides, among key benefits of big data are the lover cost of the service, and the provision of more accurate data, allowing financial market players to proactively respond to any changes.
Lots of big data companies design predictive systems to understand and manipulate data sets, digest vast amounts of data and help to make more informed investment decisions. They provide a more detailed understanding of trends and ingesting how these precise datasets can help investors to stay ahead of the competition.
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Detecting fraudulent activities and credit card thefts are no longer challenges in banking industry and turned into an almost routine procedure. Banks can protect the data of their consumers: studying financial habits and spending allows them to identify certain habits and patterns of customer behavior. Banks access huge customer data from different sources such as social media, logs and call center conversation and that data can be potentially helpful in identifying abnormal activities.
Big data is making detecting unusual transactions easier. For example, if two transactions from the same credit card take place at different cities within a short time gap, the bank is going to get red flags. Therefore, if a number of atypical purchases are revealed on the card, the account may be frozen. Perhaps this is not very convenient for those who are prone to spontaneous purchases, but in most cases, this is a justifiable measure of protection of funds.
Manual Processes Automation
Big data goes well with other disruptive technologies. The use of AI allows reducing a fairly large number of manual procedures. Such big data projects free employees from unnecessary paperwork, making it possible to rely on algorithms and automated processes. Thus, some roles are replaced by a more efficient, less error-prone and cheaper algorithm.
The algorithm acts as digital platforms providing automated algorithm-led financial planning services without the need for human advisory. It collects customer data about their financial picture and their goals through surveys and uses the data to offer financial advice. It also can be used in the form of a chatbot, addressing simple customer inquiries, walking customers through the sales cycle, offering tips, advice, all while gathering customer data to help improve the customer experience.
Customer Fringe Benefits
Personalized offers and products are not the only benefits that financial companies can provide to their customers using big data. Big data analytics and predictive analytics allows them to provide in advance the “smart” services needed by each specific client. This may be advisory services, reducing the duration of operations or simplifying some procedures.
Using the big data features for such purposes, banks and investment companies improve their user experience and get additional loyalty points. Understanding customer behavior allows companies to understand which type of person is more valuable to their business. The achieved information can be used to attract more targeted marketing activity to customers who present greater potential for higher revenue.
Top 3 Big Data Trends
It is predicted that data volumes will continue to grow ever larger in 2020 as well. The continuing use of big data will impact the way organizations perceive and use business intelligence. Some big data financial industry trends involve new concepts, while others mix and merge different computer technologies that are based on big data. It is predicted that technology will be used to determine the customer’s creditworthiness based on his social connections; to improve mortgage lending and build unified data analytics platforms.
1. Consumer Social Credit Score
To issue a loan, a large data set is usually studied, taking into account solvency, previous credit history, financial activity and other reliability factors of a potential borrower. The classic criteria evaluation system can reject or approve a loan based only on financial data.
A new trend in the financial services industry offers to take into account social and professional relations, as well as the digital activity of the user to make a decision. It is assumed that these factors are no less, and sometimes more important than dry financial statistics.
This method of determining the user’s creditworthiness will help to assess a variety of risks. For example, if a potential borrower does not have a credit history, social connections can be a plus for him. While the information that the client often changes phone numbers can be a negative factor in his assessment as a potential borrower.
2. Mortgage Lending
In mortgage lending, big data also helps to evaluate a potential buyer and calculate risks. The idea of researching the social life and Internet activity of the customer is relevant for this type of loan, however, the possibilities and capabilities of big data applications for mortgage lending are much wider.
The process of applying for a mortgage may be substantially changed in the near future. By mixing big data analytics and ML, you can get a deeper level of digging into the available information obtained not only from financial sources but also from any public, including web sites. After finishing the application, it will be compared with the data available in the company and, based on these data, the application will be approved or rejected. If there are too many discrepancies in the data, the application may be rejected or sent for human review. It also allows identifying identity theft or other fraudulent real estate transactions.
The undoubted advantage of this approach is a significant saving in the money and time of lenders, since the application does not go to the manager until it passes a series of procedures and checks by the algorithms.
Another way to use big data analytics in mortgage lending is to analyze market prices for real estate. The implementation of algorithms helps to appropriately evaluate real estate based on the analysis of similar objects, taking into account many additional factors.
3. Unified Data Analytics Platforms
Creating a unified data analytics platform is relevant for large financial companies that have several departments in various areas: investment banking, commercial banking, retail banking, etc. For the successful work of each unit, it is necessary to mine information according to various criteria using different analytical platforms. However, in such a scenario, it is very difficult to ensure the communication of different data points between departments of a big financial company.
A unified analytical platform will enable the creation of specialized data processing environments on-demand. This allows each data scientist to launch the platform using his own customized environment and through the user-friendly interface deploy machine learning throughout the organization. It is especially relevant for big financial groups serving millions of clients worldwide to have a unified solution for data processing across departments.
Big data analytical tools, built into such a financial data analytic platform, significantly improve its work, accelerating data mining, helping to systemize it. Also for such a platform, cloud big data technologies can be used to provide quick access to databases from different parts of the world and divisions of the company.
Read more articles on applying big data for business in the blog. If you are interested in implementing big data analytics to your business feel free to email Computools’s expert at firstname.lastname@example.org.
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