In a previous article I shared some of the challenges, benefits and trends of Big Data in the telecommunications industry. This time, I will focus on the financial services industry based on previous IBM studies in this industry and some personal experiences.
An Industry Without Physical Products
Big Data’s promise of value in the financial services industry is particularly differentiating. With no physical products to offer, the data, the source of the information – is without a doubt one of its most important assets. The banking and financial services business is replete with transactions, hundreds of millions of them a day, each adding a new row to the industry’s vast ocean of data. So, the question for many of the industry’s companies is how to cultivate and leverage this information to gain a competitive advantage? Investopedia says that the growing amount of data is going to be very important in the financial industry. They show statistics that 2.5 quintillion bytes of data are created every day. Over 90% of all known data has been developed in just the past few years. Companies in the industry are having to deal with an increasingly diverse and demanding customer base that insists on communicating in new and varied ways, at any time of day. Although structured industry data continues to grow in size and scope, it is the world of unstructured data that is emerging as an increasingly large and important source of information. Investment bankers, financial advisors, account managers, and other employees must have timely and relevant information to make better decisions, without neglecting the industry’s regulatory requirements. The banking and financial services industry is not immune to the growth of social networks as its reputation and brands are discussed by its clients and their personal networks. The creation of useful data now extends beyond the control of banks. Most studies show that although the financial services industry is generally one of the most technologically advanced, Big Data initiatives are still in the planning and development stages. The industry is adopting a pragmatic, results-focused approach. Big Data’s most effective strategies identify business requirements first, and then leverage existing infrastructure, data sources and analytical solutions to support the business opportunity. Here are the main approaches.
Customer-focused analysis dominates Big Data initiatives
A high percentage of industry initiatives are focused on customer-centric objectives. They are struggling to provide better customer service to their customers. Big Data relies on a scalable and extensible information foundation. The promise of significant and measurable business value can only be achieved if organizations implement an information foundation that supports the rapid growth, speed and variety of data. The ability to connect data silos throughout the organization has been a Business Intelligence challenge for years, especially in banks where mergers and acquisitions have generated numerous and costly data silos. This integration is even more important, but much more complex with Big Data. Although some banks are already developing pilots with Hadoop and other associated technologies, there is still a long way to go.
Variables Financial Industry Uses in its Big Data Algorithms
According to Home Equity Wiz, there are a number of variables that come into play. Here are some factors that financial industry big data algorithms rely on.
Your Income and Your Credit Score
Big data algorithms rely heavily on credit score data. A borrower will need to prove that they have had a steady income for approximately two years in order to secure a home equity loan or HELOC. Your potential lender will also run a credit check to see what your current credit score is. Essentially, the lender is assessing the risk of giving you this requested money. Most lenders will require you to have a credit score of at least 620. A higher credit score will improve your chances.
Equity Amount
AI tools also look at equity value. In order to think about these opportunities, you need to have 15 to 20 percent equity in your current property investment. The outstanding mortgage balance and the line of credit or loan must not exceed 85 percent of your home’s value when combined. When you have a lower loan / value ratio, this will increase your chance of loan approval.
Debt and Income Ratio
Lenders use big data algorithms that take a very intricate look at your debt and income ratio. This ratio takes into account your monthly debt, including the amount that you would be taking on with your new loan. This number is divided by your monthly income. When your overall ratio is very large, this shows a lender that you have a higher chance of defaulting on your loan. This isn’t always a risk that a bank is willing to take on. Some lenders will allow you to have a debt and income ratio of 50 percent, but the typical amount is around 43 percent.
Do Requirements Change Based on the Lender That You Choose?
Every financial institution has the right to conduct business how they see fit. Certain established customers may receive different requirements, fees and interest rates than someone who has just walked through the door. Each lender has their own limitations when it comes to the risk that they are willing to take on. This is because there aren’t a lot of federal and state regulations when it comes to home equity loans and HELOCs. There is a maximum interest rate that is set by the United States Treasury, but you won’t find many laws applying to fees and costs. You can usually find some information on lender’s websites regarding rates and fees. You can also call to set up an appointment for more information. Don’t forget to look into credit unions. They have fewer requirements in most cases.
Big Data is Transforming the Financial Industry
The financial industry is undergoing a number of changes in 2019. We need to be aware of the role that big data is playing. Machine learning and AI are going to have a profound effect on the way that financial actuaries issue loans, so consumers need to adapt accordingly.
