The finance industry is among those who are figuring out ways to use revolutionary Artificial Intelligence technology. Artificial Intelligence refers to machines or systems replicating human intelligence and performing tasks like humans. Al intends to enhance human skills and capabilities to help them do their work easily and effectively. The future of AI in finance is thoroughly discussed in this blog.
Machine Learning is an essential application of Artificial Intelligence. It mainly focuses on building systems that perform based on the input data without requiring numerous human interventions such as coding, etc. According to a study, 83% of Indian financial institutions say AI improves the customer experience.
The world of finance has grown a lot since the emergence of Artificial Intelligence and Machine Learning. It encompasses capital markets, money, investments, banking, leverage or debt, credit, and the development and regulation of financial institutions. With the growing technical environment, pictorial presentation and trend analysis have become quite common, and Artificial Intelligence plays a large part in it. The future of Al in finance is quite bright as, traditionally, it was done manually and consumed a lot of time, but now you can do that in the blink of an eye. Not only can you manage the flow of money, but you can also optimize profitability, calculate taxes, measure cash flow, ensure compliance, and much more.
Financial services adopted Al primarily to use it for Know Your Customer (KYC), Anti-Money Laundering (AML), and fraud detection operations.
Here are the top 10 uses of Al in financial services:
The use of artificial intelligence (AI) is a wonderful way to improve credit scoring by leveraging more data to provide individualized credit scores based on current income, career opportunities, recent credit history, and earning potential in addition to earlier credit histories.
Due to AI’s capacity to automate labor-intensive activities, labor costs can be decreased while quality is raised.
The practice of transforming internal and external financial duties and responsibilities for a higher time, cost, and resource savings is known as AI-based finance process optimization.
AI assesses unstructured data about potentially dangerous actions or activities in an organization’s operations. AI systems are able to recognize behavioral patterns associated with previous events and translate them into danger indicators.
The development of AI in the financial markets has been quite remarkable. For instance, supervised learning models can accurately forecast the behavior of creditors or customers using the vast amounts of already available data.
AI is a broad term that refers to the use of particular types of investigation to carry out tasks ranging from driving a car to fraud detection via rules-based calculations.
AI supports managers in managing investment portfolios and assures logical, data-driven decision-making. Long-term investment profitability is therefore increased as a result.
The applications of AI in the financial sector are astounding. The banks are using AI for a variety of purposes, including identifying fraud, improving customer experience, tracking customer behavior to offer more individualized services, examining consumer credit histories to forecast risks connected with loan allocation, and many others.
The speed and accuracy with which AI can process huge amounts of data has the potential to revolutionize regulatory compliance.
Businesses can now track the actions and behaviors of their customers in real-time thanks to AI. Companies can use this data to personalize marketing campaigns, provide better customer service, and increase client retention rates.
An Asset Management Company gathers funds from either individual or institutional investors and utilizes the same to raise client capital. An asset management company determines the best method of asset utilization to reap maximum profits for clients. The AMC accumulates the fund from the public and considers various risks, including market, political, etc. In the same way, it invests in both high-risk and low-risk securities, including debts, stocks, bonds, pension funds, and others. AMC is considered a buy-side entity, given its need for information that would help it make smart and lucrative investment decisions.
AMC uses finance for the following purposes and more:
The Automated Trading System is a synonym for Algorithmic Trading. Algorithmic trading is using computers programmed to follow a defined set of instructions for placing a trade to generate profits at a speed and frequency that is impossible for a human trader. This kind of trading mainly involves the use of computer-assisted techniques such as Naive Bayes, Decision Tree, and Random Forest, so it mainly depends on Al. Hence, Al in finance plays a very crucial role.
Credit intermediation acts as a middleman for two parties in a lending process by proposing and presenting credit agreements to consumers. Credit intermediary uses AL as the major work of credit intermediary involves general financial planning, career development, lending, retirement, tax preparation, and credit. Al also helps in analyzing a client’s data so that the credit intermediary can analyze the client’s creditworthiness.
Blockchain and Artificial Intelligence (AI) solve different tasks, but they can work together to improve many processes in the financial services industry, from customer service to loan application reviews and payment processing. Nowadays, capital markets also use blockchain facilities such as Dogecoin, Bitcoin, etc. The Blockchain facility provides more security for managing funds.
In India, AI credit cash apps are a major financial risk, as they come with major downsides. Some of the other major AI-related financial risks are explained below:
Although Artificial Intelligence plays a crucial role in finance and has many benefits, there is also a breach of confidentiality. There are multiple ways to find and post the data, like your own. Data Management becomes quite difficult when similar data are sometimes presented in different formats. Concentration risk is the potential for a loss in value of an investment portfolio or a financial institution when an individual or group of exposures move together in an unfavorable direction. The loss due to concentration risk loss is quite serious and generally becomes impossible to recover.
Due to the bright future of Al in finance, it has become quite necessary to be financially educated to understand and analyze the flow of money and the workings of any company. Financial education mainly includes management, auditing, economics, accounting, etc. Financial education helps not only those who are working in finance but also those who are not working in this field, as more or less each and every kind of business has some element of finance that is crucial for their effective work, and not only businesses, even households, need to have the basic knowledge of financial management so that they can make their budget accordingly.
Now that the financial markets have become quite vulnerable to data security and fraud, it has become important to set up financial consumer protection measures. Financial consumer protection includes the establishment of laws and regulations to protect the consumer from any activities that are harmful to the consumer related to the purchase and use of any financial information.
A consumer protection policy was introduced to protect consumers from all kinds of fraud. According to this consumer protection policy, a Consumer Protection Act was established, which acts in favor of consumers, resolves their complaints, and seeks to ensure the health and safety of consumers while using goods and services.
The Consumer Protection Act of 2019’s primary goal is to safeguard and advance consumer interests through the timely and affordable resolution of their complaints. All businesses kinds in India are subject to the Act, regardless of whether they are manufacturers, traders, or suppliers of goods or services (also including e-commerce firms).
Principle 2: Oversight Bodies
An oversight body should be responsible for consumer protection and the authority to mandate fulfillment. These bodies should have to be independent so that they can give their unbiased judgment. There should be cooperation between other financial services oversight authorities and the authorities or departments in charge of sectoral issues.
Principle 4: Disclosure and Transparency
Appropriate information should be provided at all stages of the relationship with the customer. The information provided should be transparent and honest to the consumer. Ethics for Al technologies require public trust, disclosure, and transparency. Transparency would be bad if the explanations were wrong, aka false positives. And not all of it needs to be explained in detail if the use case is not regulated. Transparency is most important in highly regulated areas, credit underwriting being the one we’re in today. However, there are a number of other applications of Al where similar requirements to satisfy regulation or document clear model explainability exist, such as healthcare or government services.
Principle 7: Protection of Consumer Assets
The authorities should make sure that consumer assets are protected, and data should not be lost while we use Al in finance. Asset protection is a legal strategy that allows the debtor to prevent creditors from accessing or seizing their valuable assets.
Principle 8: Protection of Consumer Data Privacy
Data should be protected, keeping in mind the privacy of consumers. If the consumer’s data is protected, only then can AI be considered beneficial in finance. Otherwise, it will be only considered as a hindrance in the future of finance.
Algorithmic bias means using biased data to create Al, which ultimately leads to other social consequences. We can minimize it by performing tests and other best practices.
The Explainability Conundrum
The term “explainability” means the pre-modeling, model development, and post-modeling, or determining how much explanation is needed in the data to make sure that Al works effectively.
Training, validation, and testing of Al models to promote their robustness and resilience
To promote their robustness and resilience, proper training, validation, and necessary testing should be performed so that there should be no errors or threats while performing the work on Al. It is especially important in the world of finance.
Governance of AL systems and accountability
With the rapid growth of Al, it has become a necessity that we raise questions about the governance and accountability of these technologies. We should question how the data input will be used, secured, and stored. We should also ensure that Al is transparent, compliant with guidelines, and should not be used in any unfair way.
Other sources of risks in all use cases in finance are regulatory considerations, employment, and skills.
These are seven other sources of risks in Al, but this list is not exhaustive:
The future of AI in the domain of Finance is bright. But there are numerous drawbacks as well, related to data security and governance. Several steps have been taken to govern Al and to ensure transparent working and minimize data loss, but it still has a long way to go, both in development and in ensuring transparency. Al has become an important part of the world of finance, not only in the world of finance but also in our daily lives. For an in-depth exploration of this topic, we’d suggest you enroll in UNext Jigsaw’s Executive PG Diploma in Management & Artificial Intelligence.