One thing common between finance and data science is they’re both very…numbersome. So it’s only natural that data science is used extensively in finance. Fintech, banks, insurance, the stock market, finance vertical of a business- data science can be used everywhere. If you have a strong blend of math, finance and programming, then “quantitative analyst” or “quant” is the job for you! Psst. They earn a LOT. More than even your college’s Xerox guy.
Anyway, let’s get back to Data science in Finance, and look at four main use cases:
1) Risk Analytics and risk modelling – Risk Analytics has been around for awhile and now with advanced data science methods, risk analysts are getting better and better at detecting and managing risk. Quants use statistics and data science to build models which will aid in managing risk and making decisions regarding investments, pricing etc. Credit risk is an area where data science is used extensively. Deciding if a person would pay back their loan can depend on so many factors-their past credit history(have they been loan defaulters before?), their social media(what kind of people are they? Are they trustworthy?What memes do they like?Okay, maybe not the memes part), their current assets , the industry they’re working in (Is the industry stable? Is it on a downward trend?) and so much more. With more data and more people, complex and scalable modelling and scoring algorithms are required for this purpose.
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Want to get your hands dirty with credit risk modelling? Click here to get amazing datasets! Go on and unleash your data wizardry skill!
2) Financial fraud detection –
Credit card fraud losses globally are expected to exceed $35 billion by 2020 according to a Nielsen report . That’s like more than 12 times the money Avengers:Endgame made. With big data being available now, data science is being applied widely to detect and prevent
Financial fraud. Anomaly detection, clustering and classification are usually used for this.
Want to learn more about anomaly detection? No problemo! Here is the link to the most extensive collection of information and resources for anomaly detection.
At Supervised Learning, we also have datatales for Credit Card Default Analysis, clustering credit card dataset and Insurance claims!
3)Predicting finances of a company (cashflow, receivables etc) – Any company without a firm grip on it’s finances won’t survive long. With more and more companies becoming data driven, it’s easier than ever to have knowledge about what has happened (descriptive analytics), why it occurred (diagnostic analytics) and what will occur in the future(predictive analytics). So many FP&A (Financial Planning and Analysis) software companies are now embedding data science in their products to predict, plan and analyze better. They use it for cash forecasting,variance analysis, anomaly detection etc. An example of an Finance software using data science extensively is Highradius.
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4) Algorithmic Trading: It’s where computer algorithms dynamically asses market situation and enable buying and selling tens of thousands of trades per second.
Highly intricate and complex mathematical models work in jet speed to determine trading Data Science in Finance 2 strategies in split seconds. Quants are the ones who build these data models.
Data science is used to find patterns in the available financial data which helps in predicting the future and thus device intelligent strategies to place trades automatically.
Quick question, can time series experts be considered as fortune tellers?
Here’s a dataset incase you want to try something out in Algorithmic Trading while you facepalm for my bad joke.
Data Science is extensively used for Finance related functions and hence there are a lot of packages and libraries out there just for this in both R and Python! Here are a few examples for Python: Quantlib, Pyfolio, Quandl,Zipline, Quantdsl, pyfin, tia, backtrader,dynts.
At supervised learning, we have 5 data-tales related to finance and a considerable number of use cases too!
Join us to learn more 🙂
