Introduction:
Data Science is basically defined as an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
- By using it we gain problem solving skills.
- It is having High demand as it explores many areas of technology.
- Now a days Analytics is everywhere, and it impart the analytics more deeply.
- It is only becoming more important as it has a range of related skills.
Role of Data Science in industry revolution:
Data Science is being considered as the future technology for modern industry because it is now able to capture the entire domain of science under a single umbrella. It has now expanded its application in association with Artificial Intelligence to every field of science which includes, Retails, E-Commerce, Web & Social Media, Banking, Supply Chain, Health Care, Insurance, Entrepreneurship and Start-up, Finance & Accounts.
Now if we consider the finance then for every organization, the management of finance is very important. The finance is now a days related with many other aspects, so the financial analysis is very important and hence to be a good analyst companies are looking for. Now it is completely relied on data to extract valuable insights for decades.
However, the rise of data science and machine learning has brought upon a new era in the field.
So, to get you up-to-speed with the latest developments, in this post, we’ll explore the importance of data science in Finance. More specifically, we’ll walk you through the top 5 ways data science is reinventing the industry:
- Fraud Prevention
- Anomaly Detection
- Customer Analytics
- Risk Management and
- Algorithmic Trading
In Finance the most important top 5 areas where the Data science has made the great contributions are discussed as below because as we listed above are the major areas where we need to put more emphasis to strengthen the finance.
Fraud Prevention
- Fraud prevention is a part of financial security that deals with fraudulent activities, such as identity theft and credit card schemes.
- We must need to know How Do Financial Institutions Prevent Fraud?
- Abnormally high transactions from conservative spenders, or out of region purchases often signal credit card fraud. Whenever such are detected, the cards are usually automatically blocked. Then, a notification is sent out to the owner.
Role of Data Science in Preventing the Fraud?
- The role data science plays here comes in the form of random forests and other methods that determine whether there are sufficient factors to indicate suspicion.
- Data Science has provided the better security advancements with facial or fingerprint recognition have added layers of authentication.
- 3D passwords, text messages confirmation and PINT codes have also massively backed the safety of online transactions.
- One another technique is also very much useful i.e. Pattern recognitions which is a application of data science.
- It requires the use of machine learning algorithms. That said, data science has substantially improved fraud prevention in more ways than one.
Anomaly Detection
- Depending on this information, they assign expected worth of each client
- When we talk about data science in Finance, we cannot possibly skip anomaly detection. Unlike Fraud Prevention, the goal here is to detect the problem, rather than prevent it.
- The reason is that we cannot classify an event “anomalous” as it happens but can only do so in the aftermath.
- If we need to understand that How Does Anomaly Detection Work in Finance, then we can take the case study of today’s financial world.
It is not always easy to spot trading patterns with a naked eye. - The Deep learning is also another approach which has an application of Cognitive Intelligence which imparts a great contribution in Anomaly Detection.
- Through a mix of Recurrent Neural Networks and Long Short-Term Memory models, data scientists can create anomaly-detection algorithms.
- Algorithms analyse the trading patterns before and after the internal announcement of non-public information like the release of a new product or an upcoming merger.
Customer Analytics
- Investors and higher-ups don’t like uncertainty when it comes to major deals, so there exists a need to measure, analyse and predict risk. It is another important area which is very much important example of data science in Finance in the Customer Analytics field.
- Based on past behavioural trends, financial institutions can make predictions on how each consumer is likely to act.
- With the help of socio-economic characteristics, they’re able to split consumers into clusters and make estimations on how much money they expect to gain from each client in the future.
- Let us take an example, in insurance companies often use this technique to assign lifetime evaluations to each consumer. And while this is not the most precise technique, it does prove to be very solid in practice.
- If we need to analyse that how does Data Science fit into this prospect, then we need to understand the concept of Un supervised learning.
- Using unsupervised machine learning techniques, the company splits consumers into distinct groups based on certain characteristics, such as age, income, address, etc.
- Depending on this information, they assign expected worth of each client.
Risk Management
- Another important factor in finance is stability, a.k.a. risk management.
- Investors and higher-ups don’t like uncertainty when it comes to major deals, so there exists a need to measure, analyse and predict risk.
- Risk can be many things — it can be uncertainty about the market, it can be an influx of competition, or it can be some customer trustworthy-ness.
- Overall, risk management is a complex field requiring knowledge across finance, math, statistics and more.
- However, a current-day data scientist has the necessary skills for both previous positions.
- The main approach dictates that the first step is identifying and ranking all the uncertain interactions.
Algorithmic Trading
- We have Algorithmic Trading when a machine makes trades on the market based on an algorithm.
- They can happen multiple times every second with various degrees of volume.
The algorithmic trading has mitigated many of the opportunity costs that come from missing a trading opportunity by hesitation, as well as other human errors. - The algorithms consist of a set of rules which steer the decisions to trade or not.
- The algorithms consist of a set of rules which steer the decisions to trade or not.
- The way it works is the following: the algorithm develops conditions that make up a “signal”. Once they are met, this signal is sent out to the algorithm, and it makes a trade.
- The requirements for these conditions are so well-established that it takes fractions of a second between the signal and the trade to occur.
Scope and Facility @ Naresh I Technologies :
At Naresh IT you will get a good Experienced faculty who will guide you, mentor you and nurture you to achieve your dream goal.
Here you will get a good hand on practice in terms of practical industry-oriented environment which will definitely help you a lot to shape your future.
During the designing process of application, we will let you know about the other aspect of the application too.
Our Expert trainer will let you know about every in’s and out’s about the problem scenario.