The use of Artificial Intelligence (AI) comes with several opportunities to help businesses scale and excel in meeting their goals. AI is now being used in almost every field to build innovative products and services. AI-based predictive analysis is key in helping companies forecast the future.
For instance, lenders can predict the potential creditworthiness of a business by analysing historical and current transaction data.
Between 2022 and 2033, the worldwide AI-powered platform lending market size is projected to grow at a Compound Annual Growth Rate of 25%, rising from a market share of $70 billion in 2022 to $90 billion by 2033.
Despite the advances and potential of AI, the greatly discussed and debated risks of AI in finance make many businesses hesitant about adopting AI-driven tech solutions.
This blog explores the key risks associated with AI in the financial services industry and how superior AI-powered tech solutions address the risks while delivering value to businesses.
Risks of AI in Finance: An Overview
In any AI-driven system, machines such as computer systems simulate human-like intelligence. Thus machines start to mimic the behaviour of humans in three ways – thinking, learning, and taking action.
AI can learn from existing and real-time data, recognise patterns, make decisions, engage with humans, and take judgment calls, just like human beings.
Despite potential benefits, the characteristic nature of AI has made many businesses concerned about the ability of machines to make decisions on their own without accountability towards the business’s key values.
Finance is a core pillar in any business. As of 2021, it is estimated that there were 333.34 million companies globally. As more and more financial services companies embrace AI tech, they must understand the specific risks of AI in the finance sector, which services millions of businesses every day. In doing so, they can choose well-designed AI-driven solutions to mitigate the risks and drive more value.
Key Risks of AI in Finance
Here is a snapshot of key governance and control issues which jeopardise AI-generated outcomes.
Lack of Data Security & Privacy
A lot of sensitive financial data passes through the banking ecosystem. AI can assimilate and process vast volumes of data from multiple sources.
However, it is important not to extract and expose data, which can violate consumers’ privacy, even if it is not illegal. Data must also be securely managed and stored without being tampered with or leaked due to security breaches.
Biases in the System
Another growing concern is the penetration of human bias into the AI ecosystem at a much greater scale.
For instance, if a lending tool is not programmed to prevent biases against small businesses, the lender can lose out on potential opportunities.
Absence of Comprehensive Guidelines
As of now, there are no specific guidelines outlined by regulatory bodies about the usage of AI in the financial services sector. Hence, there is potential for manipulation due to the irresponsible use of AI.
Inability to Integrate With Legacy Systems
While businesses use AI-driven tools, they are simultaneously using various Legacy system software. However, some AI tools are incompatible with these software platforms, making it challenging for companies to have a cohesive tech stack.
Lack of Relevant Workforce Training
An AI-driven tool is as good as the ability of the workforce to leverage it to its full potential. Hence, businesses must ensure that their teams are well-trained in their usage.
For instance, teams must be able to set the algorithms in such a way as to eliminate the potential for bias and drive more value for the customer.
Beyond the Risks: AI-driven Exclusive Opportunities
Despite AI risks in finance, this technology has the potential to revolutionise the financial services industry in several ways. Any superior AI products address the risks and offer solutions that align with the values of the business.
For instance, the use of a cloud-based AI-driven financial analysis tool enables lenders to become more inclusive while scaling faster.
Here are some ways in which the software is adding value to lenders.
Access to in-depth data insights
Lenders can understand consumer behaviour, and market trends in great detail. AI-based predictive analytical capabilities enable businesses to forecast future trends by the minute, day, month, and year based on historical and real-time data.
Strong Data Governance Framework
Data is extracted, processed, used and stored securely due to a strong data governance framework. Due to strong governance guidelines, there is no potential for data to be manipulated or duplicated.
Superior Fraud Detection Capabilities
AI can recognise and flag irregular patterns within minutes. It can also detect the presence of fraudulent documentation and identity theft.
Traditionally, fraud detection consumes the time and effort of large-scale human intervention without guaranteeing results. But AI can do the work quickly and at scale.
Customisation of Products and Services
AI, if programmed right, can pave the way for a more inclusive, diverse lending culture that serves all customers as per their needs and requirements. Hence, rather than offering on-size-fits-all loan products,
AI-powered financial analysis tools can customise products and services based on the customer’s cash flow history and needs. Thus, lenders can reach the last customer, capture more market share, and drive revenues.
Data-Driven Underwriting Decisions
All lending decisions can be based on data rather than instincts or general perceptions of a borrower.
A superior financial analysis tool processes large volumes of bank statements, Goods and Services Tax Returns (GSTR), and other financial data to arrive at an accurate creditworthiness score.
Thus, underwriting decisions will be made more efficiently, quickly, and accurately, which forms the foundation of any successful lending business.
Final Note
The adoption of AI is one of the top shifts across the financial services industry. In particular, lending businesses can ensure that they can seamlessly manage the risks of AI in finance by adopting superior-quality tech solutions.
Businesses can partner with a technology specialist, which comes with an in-depth understanding of AI’s potential and liabilities and builds the technology that eliminates the risks while driving results.
Presica’s comprehensive and seamless financial data analysis solution simplifies and speeds up the process through automation. The software provides actionable insights on a customisable dashboard, thus helping companies make informed business decisions.
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