There is definitely no short supply of data – in fact, there is probably an excess of data available today when accounting for traffic running through social media, transactions, real-time market feeds, and other places. This means there are explosive amounts of data available for the financial sector. The variety of data available and the speed of accessing data have also grown astoundingly. This can create two scenarios for organizations – they either harness the data and use it for innovation or stand by in awe at the massive amounts of data they are presented with. Since businesses are in business to succeed, they have taken the approach of hiring data scientists to help them sort through data.
A data scientist takes a data set, analyzes it from all angles, and uses it to make inferences or predictions that can possibly lead to beneficial discoveries.
Now, let’s see some of the most widely used data science techniques in the finance industry.
It might seem that a data scientist is concerned with concrete numbers and figures, solidly objective data. However, there are services and tools available that help them analyse people’s sentiments- also known as opinion mining.
A few examples are Think Big Analytics, MarketPsy, Capital, and MarketPsych Data. These firms and programs analyse text, language processing, and computational linguistics to consolidate the information into usable material to improve business.
Insurance firms build and use algorithms to compile relevant data from the online marketplace, for instance, from Twitter feeds. These feeds provide a massive amount of data when concerned with specific impactful events such as disastrous weather or terrorist attacks.
These feeds can also be mined by organisations to find trends when monitoring new products or services or responding to widespread issues that might affect the image of their brand overall. For banks, sentiment analysis can examine recorded phone calls to find ways to reduce customer turnover and recommend ways to improve customer retention. Many of today’s banks are customer-focused so having a service to analyze data about how or what customers feel toward them is a key factor to the success of the bank.
Reduced online lending risk
Data scientists have found ways to use the variety, frequency and amount of data available in the online marketplace to enable finance companies to offer credit online with very little risk.
Sometimes investors won’t or don’t access credit because of the lack of a way to give them a credit rating. However, it is essential for lenders and financiers to be able to measure the risk when considering handing out credit.
Internet finance companies have emerged thanks to big data and the scientists that analyse the data, developing ways of managing risk to be able to approve online loans. A good example of online lending enabled by big data analysis is Alibaba AliLoan, an automated online bank that offers small and flexible loans to online entrepreneurs. Recipients are typically with no collateral, which makes securing loans from traditional banks nearly impossible.
Alibaba monitors e-commerce payments and payment platforms to understand customer behaviour and their financial strength. They will analyse a customer’s ratings, transactions, shipping records, as well as other information, and can generate loan cap for the customer and the associated level of risk.
Alibaba also uses external third-party verifiers to cross-check their own findings. Other resources might include tax records or utility bills. Once the loan is granted, Alibaba continues to track customer behavior and spending patterns of the loan provided to them and will also monitor business development.
Lenddo and Kreditech are other companies that utilize data scientists’ abilities to manage risk for recipients of automated loans. These companies have developed credit scoring techniques that are helpful and innovative in determining a customer’s creditworthiness. Sometimes even data from online social networks is used to gauge a customer’s creditworthiness.
The finance industry and real-time analytics
The finance industry can’t just rely on having a compilation of data that is easily accessible. What really matters when it comes to data is when it’s analyzed. It’s just not possible to rely on the data itself to make an informed decision – it must be analyzed at regular intervals. Sitting on compiled data for extended lengths of time decreases the likelihood to seize critical opportunities and increases the possibility of things going awry. However, by using the skills of data scientists the lag in time that once was an issue in the finance sector is no longer worrisome.
Reducing the occurrences of fraud may be one of the most important contributions of real-time data analytics when it comes to protecting the consumer’s information. Banks and credit card companies, along with others, have made it common practice to prioritize securing fundamental account information with the use of big data. They want to ensure your employment status is accurate and your location is up to date in the case that something occurs that is out of the “normal” behavior pattern. “Normal” behavior patterns include characteristics such as spending patterns, account balances, credit history analysis, and other general – yet important – details.
With real-time analytics, the company is able to notice almost instantaneously when something seems out of character for a customer and can trigger a flag on the account. Usually, the account will be suspended in case the activity is fraudulent and the account holder is notified of the activity and suspension.
Big data real-time analysis is also beneficial for improving credit ratings. An accurate credit rating literally requires amassing an ample amount of current data, placing less focus on historical data. Since credit ratings are such an important part of securing almost any kind of loan or asset, it’s important that the applicant has an accurate and up-to-date credit rating to determine the level of risk of an individual.
Being available in real-time gives a reasonable assumption about the financial capacity of a customer. With the online marketplace readily available, many categories used for calculating credit ratings are easily accessible. These can include transaction histories, business operations a customer may be engaged in, and other assets that are held by the customer. Another benefit to customers is the ability to provide accurate pricing for various products and services. For example, in the case of a loan, the customer may be able to secure a better interest rate on borrowed funds if their credit rating is higher than when they previously applied for financing.
Big data continues to transform the landscape of various industries, particularly financial services. Many financial institutions are adopting big data analytics in order to maintain a competitive edge. I hope the examples I illustrated will give you a clear picture of how big data is used to transform financial services sector.
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