Artificial Intelligence in Finance

AI in Finance

“Life is a race … if you don’t run fast … you will be like a broken egg” – Virus Character in 3 Idiots.

“Speed matters in business – plus a high-velocity decision-making environment is more fun too. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive” – Jeff Bezos, Amazon Founder and CEO.

The two quotes above are close enough to state the same meaning. However, the first one is mostly mocked created by the perception of the film while the other one is greatly admired created by the perception of Jeff Bezos’s success. While we don’t have any parameters to gauge the validity of the first quote we have more than enough gauges to believe the most successful e-commerce company CEO on the globe. Today, Information is the key to wealth creation.

Businesses that extract the required information with speed and accuracy for decision making succeed more often in their ventures than others who don’t use it. One such tool that helps with faster decision making in Finance is Artificial Intelligence (AI).

AI is mainly used in the Financial Services industry to add value to the existing services, reduce costs, and save time through automation of analysis and decision making.  For banking and financial industries, AI has simplified the processes for providing complex, mundane, and tedious services to the customers in more simplified, convenient, and smarter ways to save, invest and spend their money.

It has also optimized important process segments like credit decision process, financial risk management, and quantitative trading. Apart from the above, AI has the potential to transform the said industries in the future by way of increasing the efficiency in banking, reducing the financial and legal risks, identifying suspicious activities and fraud, providing insights on investments and analyzing, detecting the public sentiment and brand effectiveness.

AI Applications in Banking

Any type of business may thrive only on the effectiveness of its marketing team. The volume of sales and closures depends upon the effective handling of the client. In the current scenario, AI is being used to decode the natural language by deploying bots on the agent’s calls to suggest accurate answers to the query for improving sales effectiveness. To reduce the response time of the queries of the agent, AI with the help of neural networks processes the FAQs and generates some critical responses each with a certainty factor.

Today, we also have tools to identify when a client would leave the bank or its agent by processing their integrated chat platforms with the bank’s websites and can act promptly prioritizing or planning their responses towards the client. Apart from the above, AI is also now used to connect the unstructured data across different databases to link and process the information much faster than humans.

Most of these bots use, cognitive technologies and predictive analytics to customize the responses by accessing the customer’s portfolio, financial goals, spending or consumption trends to advice and to automate the trades. These analytics can also predict the sales volumes, revenue generation taking the consideration of parameters like weather changes in the near future and can reconfigure the responses to the client based on the new predictive information generated.

AI Applications in Lending

AI is used by lending institutions to analyze the creditworthiness of individuals who have thin or no credit history using thousands of variables in their digital footprint. This helps them in assessing the risk, character, and capacity of the applicants with speed and accuracy. These AI applications are also used in lowering their regulatory and compliance costs. For creditors, AI can give insights to collect their outstanding debts of certain entities which is difficult for human resources to spot.

AI Applications in Accurately Detecting the Fraud Transactions

Master card uses AI’s Decision Intelligence technology to detect fraudulent transactions by analyzing various data points in real-time to approve genuine transactions and to reduce the false declines. This would reduce the risk of genuine customers and retailers abandoning the card issuer thus saving hundreds of billion dollars of annual losses to the Credit card companies. Machine learning is also used to improve regulatory compliance workflows of these companies to be efficient and accurate.

AI Applications in Image Processing

Today, we have applications in AI like Confirm.io to authenticate the customer id’s using Image Processing. Certain AI platforms like Onfido verifies this data with available public databases to give insights about the applicant’s criminal and driving records. Banks use this Image Processing Technique along with the Natural Language Processing to scan volumes of legal and regulatory documents to identify any compliance issues and fraud within minutes saving counts of human-hours.

AI in Trading and Wealth Management

AI helps trading and wealth management companies in improving the accuracy of trades by analyzing large and complex data sets and thus, automate the trades saving valuable time. AI is currently used to analyze the transcripts, filings, research reports and news to identify the changes in trends of the financial markets. Processing the above-said information AI tools like Kai gives outperformance scores to the stocks against the benchmark indices. Tools like ALPACA are used in the markets to forecast short and long term trends in the stock prices.

Conclusion

The future of the financial industry may be highly influenced by AI applications and Fintech developers. By effectively using AI the competition among the large companies may increase rapidly. The AI may help these companies to increase the revenues by significant portions in the segments of lending, investing, trading, and banking by increasing the efficiency and accuracy in the processes. AI will also become an important tool for the industry in reducing financial, legal, and regulatory risks.


References

Buster Benson, C., 2020. Ex-Amazon Manager: Jeff Bezos Is ‘Obsessed’ With This Decision-Making Style—’It’s His Key To Success’. [online] CNBC. Available at: <https://www.cnbc.com/2019/11/14/how-billionaire-jeff-bezos-makes-fast-smart-decisions-under-pressure-says-ex-amazon-manager.html> [Accessed 4 May 2020].

Emerj. 2020. Artificial Intelligence In Finance – A Comprehensive Overview | Emerj. [online] Available at: <https://emerj.com/ai-sector-overviews/artificial-intelligence-in-finance-a-comprehensive-overview/> [Accessed 4 May 2020].

Built In. 2020. AI And The Bottom Line: 15 Examples Of Artificial Intelligence In Finance. [online] Available at: <https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies> [Accessed 4 May 2020].

Medium. 2020. The Growing Impact Of AI In Financial Services: Six Examples. [online] Available at: <https://towardsdatascience.com/the-growing-impact-of-ai-in-financial-services-six-examples-da386c0301b2> [Accessed 4 May 2020].

Sigmoidal. 2020. Artificial Intelligence In Finance – Sigmoidal. [online] Available at: <https://sigmoidal.io/real-applications-of-ai-in-finance/> [Accessed 4 May 2020].