Since its beginnings, data science has helped to transform banking and finance sectors. For a long time, financial analysts have relied on data to help them draw meaningful conclusions. Even yet, a new age in this business has begun with the advancement of data science and machine learning. Automated algorithms and advanced analytical tools are used together more than ever nowadays to seize the lead.

But before we proceed, we need to very briefly explain some of the terminology which we will be using. Machine learning or ML and deep learning or DL are different aspects of data science. They use algorithms to find links between data, extract insights and draw predictions for the future. They are an important part of data science.

Okay, now that this is out of the way.

Let’s examine the top five applications of data science in the world of banking and finance:-

Fraud Prevention with the help of Data Science

Fraud prevention, which deals with unfair practices. Which include identity theft and credit card scams, is a part of financial security.

Large transaction volumes from customers made outside the region are generally signs of credit card fraud. The cards are typically automatically blocked and the owner is notified whenever such a detection is made. In this approach, banks may quickly prevent significant financial losses for themselves, their clients, and even insurance providers.

In order to decide whether there are sufficient grounds for suspicion. Data science is used in the form of random forests and other techniques by banking and finance industry. With the introduction of authentication layers made possible by face or fingerprint identification. Security innovations have decreased the likelihood of identity theft. The security of online transactions was also demonstrated by the confirmation of 3D passwords and pin codes. However, the initial security measures are what we’re most concerned with. We point out that applying ML techniques to pattern recognition is necessary. Therefore, data science has enhanced general prevention in a number of ways.

Anomaly Detection

Through a mix of repetitive neural networks and long and short-term memory models data scientists can create anomaly detection algorithms. Such an algorithm can spot whenever somebody’s trading history is well above the norm or not.

The process involves examining trade trends both before and after internal announcements of non-public information. Such as the launch of a new product or an oncoming merger. And then utilizing the volume and frequency of transactions. Discovering whether an individual is misusing the market by taking advantage of innocent investors. Therefore, data science has had a massive effect on the identification and elimination of illegal trade in industry.

Knowing Customer Analytics with the help of Data Science

 Financial organisations can forecast how each client will act based on prior behavioural trends. They are able to segment their clients, create client groups, and project future revenue from each client. Knowing this allows them to choose which individuals to appeal to and how to do so. They can also reduce their losses while dealing with clients who will generate little to no revenue. In other words, it enables them to allocate their savings in the most effective manner.

For example: This technique is regularly used by insurance companies to give lifetime evaluation to consumers. And although this is not the most accurate technique, still it proves to be very solid in practice. What role does data science play in all of this?

With the help of machine learning techniques, the company divides consumers into separate groups. These are based on specific characteristics such as age, income, address, etc. Then by building predictive models, they determine which of those characteristics is most relevant to each group.

On the basis of this information they assign expected worth to each client. After quantifying the value or range of values of each consumer. They can decide who is worth keeping and who is not worth, which helps them allocate their savings best.

Developing Algorithmic Trading Methods with Data Science Help

Algorithmic trading comprises of complex mathematical formulas and lightning speed computations. It assist financial institutions to come up with new trading strategies. Big Data has had an enormous impact on algorithmic trading and data science has become its main characteristic.

Massive data streams are used in algorithmic trading. The analytical engine’s goal is to better grasp the vast datasets in order to forecast the future market.

In a nutshell, a machine uses an algorithm to make trades on the market. These trades do not need approval from a standby analyst and can occur many times per second with different volumes.

These trades can be made in any market we choose, or even in several markets. Since many trading opportunities are losing due to human errors, algorithmic trading has reduced these expenses. These algorithms have a set of rules at their core that direct the decisions about whether to trade or not.

Risk Management

Another important factor in finance is risk management. Risk comes in various forms. Uncertainty regarding the market may be the cause. It can be an increase in competitiveness or customer reliability. Depending upon the kind. Different techniques are used for modeling and manage risk with the help of Data Science. With the access of previous data sets, decision making process is now much faster and better planned.

Also check out: How the Healthcare Industry is Being Shaped by Data Science