How Can You Apply Data Science In Finance To Prevent Fraud?

Data Science in Finance refers to systems’ set to extract insights by performing statistical analysis and forecasting future financial decisions for your business. Finance is the most complicated sector globally, but when leveraged correctly, it can be your cash cow. Data science solutions in finance have brought forth many options for organizations and individuals to grow themselves, prevent fraud, and avoid losses to some extent.

We shall talk about the most commonly discussed topic through this blog, yet a sensation — Fraud detection. We shall take it up in sequence to reinstate a wholesome understanding.

Let’s begin!

What Kind Of Information Is Required For Financial Forecasting?

Every Organisation has unprecedented amounts of data available at its disposal. Your Organization’s financial data can be categorized as follows for analytical purposes:

  • Securities And Investments: Each financial year brings in new profits for your business to invest and turn into long-term securities. These numbers result from these numbers that your business can function even during contingencies like the COVID-19 pandemic.
  • Income And Losses: As simple as we learned in high school, income and losses result from how successfully your business functions. A profit and loss statement is worth a thousand words just in numbers.
  • Bank Accounts: Your bank accounts carry the details of your transactions and give you an idea about your spendings, earnings, and more.
  • Assets: The money you invest in buying things that would bring you more business or enhance your goodwill is all categories in assets. These are available at your disposal to avail of more liquid money.
  • Debts: Just as assets are the record of things that the business owns, debt is the record of things that the company owes. Timely payments and completion of debt define your creditworthiness and strengthens your goodwill in the market.

What Are Organization Goals From Finances?

  • Increase Revenue: The base goal of any organization is profit maximization. Just as the business begins, they begin to make a percentage of profit and build on the idea of profit maximization. Their vision and mission are defined by this number, which also drives their objectives. Data science allows organizations to inflate the number based on the current trends and market situation.
  • Reduce Cost: Another way of maximizing profit is by cutting costs. Think of this as reverse engineering. If you’re unable to inflate your profit margin during a given period, then you can reduce the cost incurred for creating a product or service and increase the profit in another way.
  • Debt Service Management: Data science services significantly impact debt service management since it’s all about crunching numbers until they increase or disappear. As an organization, your role is to manage your debt judiciously. Data science techniques can help you evenly distribute your money to obligations and make more out-of-stock options.

What Are Banks’ Financial Goals?

Data Science Can Help Banks In Almost All Areas Of Work, Including The Following:

  • Risk Monitoring: The first goal of any bank is to monitor the risk of giving out loans or investing client money in other options to grow it. Data science services can help derive low-risk investment options and monitor prospective loans’ risk to ensure maximum pay-in.
  • Trade Surveillance: Here is where data science services are necessary. Trade surveillance is a process where banks watch the market to predict any dip or boom, fraud, hints for market manipulation, and more with data science techniques. The most prominent example of this could be cracking circular trading with data science.

Role Of Data Science In Finance

Fraud Prevention

If you’ve received an email from a dying woman waiting to give you her inheritance, or a royal prince forsaking his legacy and looking for the rightful heir, then you’ve been a part of mass fraud.
Bank fraud has been a part of fraud schemes for a long time, but the only difference is that the methods have changed. Data science intervention to prevent fraud has made it difficult for tricksters to fool people into taking their hard-earned money.

Now, if you’re wondering how you can detect if you’ve been a part of a fraud scheme, then here is your answer.

  • Abnormal high number of transactions compared to regular spending of customers.
  • Customers purchasing from regions different from regular regions
  • Customers with low account balances
  • Unexpected charges on account
  • Unrecognizable accounts in bank statements
  • Receiving Unapproved credit card
  • Unreasonable denial of credit

We would work on the past fraud transactions and find factors present in fraud transactions vs. non-fraudulent transactions. It will act as a base to classify future transactions based on defined features.

Anomaly Detection

Many financial events are happening in an organization. They need to find whether an opportunity is worthwhile or not and the potential risks associated with it. We try to detect such possibilities.

We try to find trading patterns and find when stocks behave unpredictably. This has helped stock traders become wealthy and successful, but sometimes, it may be fraudulent use of trading information.

Anomaly Detection analyzes trading patterns around the time any news about products has been announced. It helps find fraudulent traders utilizing insider information and making fraudulent trades. It is generally done using volume and frequency of transactions done by traders and see strange behavior. It helps law enforcement agencies to track illegal traders from innocent ones.

Risk Management

Risk Management refers to steps to ensure financial stability by analyzing significant transactions and deals to predict risk.

Banks use customer transaction data to create real-time scoring models. It helps banks in finding whether a customer is worthy of a loan or not. Another way to risk management is predicting a customer’s fraudulent behavior. The approach works by identifying customer’s uncertain interactions with banks. We monitor interactions and find patterns that are unusual and predict risk in transactions.

Algorithmic Trading

It means automating trades made in the market based on algorithms. It can happen for any amount of volume. It can happen in multiple markets. It helps traders make statistical decisions for their investment.

The algorithms are a set of rules which help decide whether to make a trade or not. It utilizes reinforcement learning. Based on the model’s performance, it adjusts hyperparameters to predict better in the future. The algorithm works by developing conditions or finding features that, when presented in trade, the machine can make a trade.

Algorithmic Trading’s potential downside is it takes an unpredictable amount of time to make a trade-in ideal conditions. Also, When something unexpected happens, You may incur huge losses.

Conclusion

We saw what financial information is available to the organizations and explored their financial goals. We further found how data science solutions have revolutionized their steps in the financial world. We can predict leaps in security and prevent losses using statistical methods. We can also make trades with caution and stop missing the market opportunities.

Originally published at https://www.zealousweb.com.

We Fuel Notions that grow! We know Web, We know your industry and we’re here to help you.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store