Comprehensive Study on the Usage of AI in Finance

Comprehensive Study on the Usage of AI in Finance.

Stephen Hawking once said that artificial intelligence (AI) is likely to be the best or the worst thing to happen to humanity. Looking at the current era, the former is certainly proving true. AI has permeated nearly every sector and has impacted millions of lives. One of those sectors is finance. According to a Forbes report, 70% of all financial service firms use machine learning to help their finances, credit scoring, prevent frauds, and more. Around 61% of employees say AI helps improve their productivity.

This article will look at the areas within the financial domain that are being positively impacted by AI as well as examine the challenges.

Benefits of AI in finance

In finance, AI assists five main activities: income generation, spending, saving, investment, and protection. Earlier, performing these tasks manually was tedious and complicated but AI has changed things.

Here are a few more benefits:

  • AI assists conversational banking.
  • It lowers false positives and human errors.
  • It reduces the need for repetitive work and hence, improves efficiency.
  • It assists in anti-fraud and risk management.
  • It helps in data analysis by extracting insights about customers, businesses, etc.

Applications of AI in finance

Today’s tech-savvy customers are compelling financial services to adapt and use artificial intelligence. AI algorithms are now being implemented by financial institutions across every financial service. Here’s how.

Personal finance

Customers look for avenues to manage their financial health and get advice about financial independence. Natural language processing-powered chatbots can offer guidance 24/7. Capital One’s Eno is an example of AI in personal finance. It was launched in 2017 and was the first natural language SMS text-based assistant from a US bank. It has functionalities that include alerting customers about account activities, suspected fraud, etc.

Credit scoring

Financial institutions need to know the creditworthiness of their customers in order to approve loans. Traditionally, credit score assessment involved analysts examining customers’ past records. Now, however, there are advanced algorithms and deep and unbiased neural networks that can do the job.

These algorithms use a large volume of past data, infer patterns and insights by looking at demographic data, check how customers are handling their finances, savings, investment, loan repayment, etc., in order to ascertain their eligibility for loans. Startups like Lendingkart, Capital Float, and Crediwatch are some organizations that use AI in credit scoring.

Fraud prevention

With the increase in online transactions and everything going digital, fraud cases are on the rise. According to a McAfee report, cybercrime is damaging the global economy with nearly 1% of global GDP lost each year. AI and its subset, machine learning (ML), offer a solid solution to fraud detection.

Using past transaction data and an AI algorithm, systems can successfully tell the difference between legal and fraudulent transactions. Here are a few strategies for fraud detection and prevention using AI:

  • Using supervised and unsupervised models together
  • Using behavioral analytics
  • Developing models with large datasets
  • Self-learning AI and adaptive analytics

A crucial point to note is that these algorithms need to be constantly researched and enhanced as cyber criminals frequently adopt new technologies to engage in fraud. Models/algorithms run the risk of making false predictions if they are not aligned with new methods of fraud.

Algorithmic trading

In trading, the saying ‘Time is money’ fits like a glove. Traders need to be fast and accurate in order to turn profits. This is because by the time one grasps the market, graphs, trends, and other patterns, it’s already late. Artificial intelligence (AI) with trading algorithms can tip the balance in favor of traders.

AI-powered algorithmic trading systems are a mix of state-of-the-art deep learning networks and machine learning algorithms. They can perform analyses and execute complex decisions in split seconds. Financial institutions and individuals alike can build their own trading systems by using these algorithms. For example, AI Autotrade, a subsidiary of RegalX and Regal Assets, is developing fully autonomous trading machines that combine technical analysis with AI and self-learning algorithms. Their task is to manage deposits in order to make a profit.

Process automation

In the finance sector, automation eases the burden on finance professionals by handling tasks like transaction processing, auditing, compliance, data entry, etc. These are resource-intensive and repetitive tasks that can be easily automated to help financial institutions reduce manpower, speed up work, and minimize errors. According to an Ernst & Young report, around 50-70% of cost reduction can be gained by automating such manual tasks.

Even customer experience can be automated. There are natural language processing-powered chatbots that can effectively solve customers’ basic queries. They can also help onboard new customers, create accounts for them, and perform KYC (know your customers) validations in minutes.

Leading financial firms including JP Morgan Chase use robotic process automation (RPA) to perform basic tasks like extracting data from forms and complying with KYC regulations.

Challenges of AI in finance

While AI offers plenty to the finance sector, it has its share of challenges. Here are a few.

Security and compliance

This is one of the biggest challenges in the finance domain because of the volume of data collected and the confidential nature of the same. Data breaches are a serious threat to customers’ financial health, which is why data storage, accessibility, etc., must be addressed extremely carefully.

Data quality

‘Garbage in, garbage out’ is a popular saying in data science, and nowhere is this more important than in finance. Wrong predictions for fraudulent transactions can lead to massive financial losses, while loan rejections because of incorrect credit scoring by AI systems can upend the lives of customers. This is why data must be clean and come from trusted sources.

Dimensionality reduction

Data in the finance industry contains thousands of data points and is very complex. Applying algorithms on such feature-rich data can either cause a model to perform well or poorly. Hence, careful analysis and feature selection must be done and dimensionality reduction techniques applied to make the data ready for use.

Future of AI in finance

AI is already doing wonders in the finance domain but as discussed, there are key challenges that need to be addressed. Ensuring transactions are secure, legal, and easy should be the target. Currently, blockchain technology is also on the rise. Its security features can be incorporated into the finance domain to make AI systems more secure and transparent, which will, in turn, develop trust between customers and financial institutions.

According to reports by Oberlo, the number of businesses adopting AI grew by 270% in four years with more than nine out of 10 leading businesses having ongoing investments in AI. As much as 62% of customers are willing to submit data to AI to improve their experience. These facts are an indication that AI is making waves and will continue to.



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