Exploring the Role of AI in Financial Services | AI in Finance
Artificial intelligence (AI) and machine learning are used in the financial industry for a range of applications, including task automation, fraud detection, and chatbot assistants. According to a recent AI in Banking survey, the vast majority of banks (80%) are well aware of the potential benefits that AI may provide.
The usage of artificial intelligence (AI) by financial institutions (FIs) will accelerate as technology progresses, consumer acceptance grows, and regulatory environments shift. By giving their customers access to their accounts and financial advising services around the clock, banks may greatly enhance the customer experience and minimise time-consuming operations.
What is Artificial Intelligence (AI)?
Artificial intelligence refers to systems or technologies that execute tasks by mimicking human intellect. AI is a very important economic asset since it is intended to significantly enhance human abilities and contributions.
What is Artificial Intelligence in Finance?
Artificial intelligence (AI) in finance is the application of technology such as machine learning (ML) to improve how financial organisations evaluate, manage, invest, and safeguard money.
How is AI driving innovation in Finance?
Financial operations have historically relied significantly on human labour, including data input, data collecting, data verification, consolidation, and reporting. The finance function tends to be expensive, time-consuming, and sluggish to change due to all of these manual tasks. At the same time, a lot of financial procedures are predictable and well-defined, which makes them excellent candidates for AI automation.
Companies were able to concentrate and standardise their financial operations thanks to the development of ERP systems. Early automation using AI was rule-based, which meant that when a transaction or input was completed, it would be processed according to a set of preset rules. These systems automate financial activities, but they lack the agility of current AI-based automation, need a lot of human maintenance, and update slowly. In contrast to rule-based automation, AI can handle more complicated circumstances, such as the total automation of dull, manual tasks.
Increasing automation equals greater accuracy in your financial procedures. With people, high-volume, boring operations like invoice input can cause weariness, burnout, and mistakes. Computers, on the other hand, are not bound by these limitations. They may also handle far more transactions in a particular time span. As a consequence, the finance team has better data to work with and more time to focus on putting that data to use.
What are the uses of artificial intelligence in financial services?
Examples of AI in finance
Companies now use AI-driven technologies to help them stay up with the rapid pace of development. 85% of company leaders desire assistance from artificial intelligence, according to a 2021 survey.
These are three typical ways businesses are utilising artificial intelligence.
First, businesses are embracing artificial intelligence to offer smart categorization and smart recognition, automating manual procedures like accounts payable processes.
Second, automated financial closure procedures allow businesses to refocus staff efforts from manual data gathering, reporting, and consolidation to analysis, strategy, and action. Scenario modelling and unbiased forecasting are key components of smart prediction.
Lastly, businesses are introducing AI-guided digital assistants that facilitate content discovery and task completion wherever you are. Finance departments, for example, may use digital assistants to alert teams when spending is out of compliance or to automatically submit expense reports for speedier payment.
With major economic benefits and demand from tech-savvy consumers in mind, let’s explore how FIs are using AI algorithms across all financial services:
AI in Personal Finance
Because customers are ravenous for financial independence, the ability to regulate one’s financial health is pushing the adoption of AI in personal finance. AI is a must-have for every financial institution that wants to be a market leader, whether it’s delivering natural language processing-powered chatbots with 24/7 financial advice or personalising insights for wealth management solutions.
AI in Consumer Finance
The ability of artificial intelligence to detect and prevent fraud and cyberattacks is one of the most critical business cases for artificial intelligence in banking. Customers need safe accounts from banks and other financial institutions, especially with online payment fraud losses anticipated to reach $48 billion per year by 2023, according to Insider Intelligence. AI has the capacity to examine and identify abnormalities in patterns that humans might otherwise miss.
AI in Corporate Finance
AI has a significant impact on corporate finance because it can more precisely detect and analyse credit risks. Machine learning and other artificial intelligence (AI) technology may improve loan underwriting and minimise financial risk for firms looking to increase their value. Although corporate accountants, analysts, treasurers, and investors aim for long-term growth, artificial intelligence (AI) may help minimise financial crime by improving fraud detection and detecting anomalous behaviour.
What are the benefits of AI in Finance?
Using AI in banking has enormous advantages for work automation, fraud detection, and providing individualised suggestions. The following are some of the ways AI use cases in the front and middle office can revolutionise the financial sector:
Enabling seamless & round-the-clock Customer Engagements
AI enables financial companies to speed up and automate formerly manual, time-consuming operations like market research.
In order to estimate future performance and discover patterns, AI can swiftly analyse enormous amounts of data. This enables investors to track investment growth and assess possible risks.
Minimising the need for tedious effort
AI and ML may enhance the whole customer experience for banking consumers. The advent of online banking (contactless banking) reduces the need for face-to-face interactions, yet the move to the virtual world may increase endpoint vulnerabilities (e.g., on cell phones, computers, and mobile devices).
Several fundamental banking transactions, such as payments, deposits, transfers, and customer support inquiries, may be automated using AI. AI can also handle credit card and loan application processes, including approval and denial, with near-instant answers.
Reducing false positives and human error
Personal data can be mined and utilised to decide coverage and premiums in the insurance industry.
AI may also be utilised in cybersecurity, especially to detect fraudulent transactions. AI may highlight aberrant behaviour, automatically inform both the institution and the consumer to verify the purchase or transfer in real-time, and take action to fix it by continuously monitoring purchase behaviour and comparing it to previous data.
Saving Revenue
By 2025, North American banks may save $70 billion by automating middle-office functions using AI. In the coming years, the implementation of AI technology in banks is anticipated to yield significant cost savings. According to recent projections, the potential savings from AI applications in banks could reach an impressive $447 billion by 2023. Interestingly, the bulk of these savings, which is estimated to be around $416 billion, is expected to come from the front and middle offices of banks. These numbers reflect the tremendous potential for AI to revolutionize the way banks operate and serve their customers, leading to increased efficiency and profitability.
What are the risks of not adopting artificial intelligence in finance?
According to the previously cited “Money and Machines” survey, 87% of business leaders feel that firms that do not rethink finance procedures would face threats such as:
- Losing ground to competitors by 44%
- 36% of workers who are more stressed
- 36% of reports are inaccurate.
- 35% decrease in staff productivity
Businesses that take their time using AI risk becoming less appealing to the next generation of financial experts. 83% of millennials and 79% of Generation Z respondents stated they would prefer a robot over their company’s financial personnel. Millennials are nearly four times as likely as Baby Boomers to want to work for a firm that uses artificial intelligence to handle money.
What does the future of AI look like for the Finance and FinTech sectors?
Due to increased client demand for digital goods and the threat of tech-savvy startups, financial institutions (FIs) are swiftly embracing digital services; by 2021, worldwide banks’ IT investment will climb to $297 billion.
FIs are under pressure to increase their IT and AI expenses in order to meet increased digital needs as millennials and Gen Zers overtake baby boomers as the largest target customer group for banks in the United States. Because 78% of millennials avoid visiting a branch if possible, these younger clients favour internet banking alternatives.
While the shift from traditional banking channels to online and mobile banking was already underway prior to the pandemic due to increased opportunity among digitally native consumers, the coronavirus dramatically accelerated the shift as stay-at-home orders were implemented across the country and consumers sought more self-service options. According to Insider Intelligence, internet and mobile banking penetration among US customers will climb to 72.8% and 58.1%, respectively, by 2024, making AI deployment important for FIs aiming to be successful and competitive in the developing sector.
Get started with HyperSense AI for Financial Services
Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.
Request a demo