AI is revolutionizing efficiency, reducing costs, and addressing risks in financial services. What does this mean for the future of the sector?
AI is transforming the financial sector by enhancing efficiency, cutting costs, and tackling new risks. In this blog, we will explore how AI is reshaping financial services and what implications it has for the future of the industry.
Monetary authorities, facing the intersection of monetary policy and financial stability, are increasingly embracing AI to optimize their operations and address emerging threats. These threats are growing due to massive security breaches.
According to a Deloitte report, 79% of financial institutions worldwide have implemented some form of AI. The global market for AI in financial services is expected to reach $45.6 billion by 2027. Growing at a compound annual growth rate of 22% from 2020. This figure demonstrates its rapid adoption and growth.
This widespread adoption of AI is redefining how financial data is managed and decisions are made. The technology’s ability to process and analyze large volumes of data allows financial institutions to perform tasks that previously took twice or three times as long. Not to mention, it has eliminated the risk of human error.
An Accenture study reveals that banks using AI in their operations report a 30% increase in operational efficiency and a 25% reduction in operational costs. This significant impact is driving other institutions to follow the lead of those who are ahead. They have become a great example due to the results achieved.
Impact of AI on financial services
Today, AI is the go-to tool for financial institutions to identify complex patterns. It helps optimize time in operational areas such as payments, loans, insurance, and asset management. We all know that time is money, and the more you optimize spending, the higher the profitability.
Let’s look at some specific examples where this benefit is evident. For instance, AI-powered chatbots enable faster and more cost-effective customer service. Moreover, the improvement in fraud detection and regulatory compliance provided by AI is unmatched by human capabilities.
In payments, AI addresses historical challenges such as Know Your Customer (KYC) and Anti-Money Laundering (AML). This reduces costs and enhances the bank’s value proposition. Additionally, it helps mitigate fragmentation in correspondent banking, a concerning global trend. This fragmentation occurs when international banks have to use multiple channels and agreements to transfer money between each other.
Instead of having a single, simple system, each bank may be connected with several other banks separately. Making transactions slower and more expensive. AI helps streamline and enhance the efficiency of the correspondent banking system. Reducing fragmentation and improving the effectiveness of international transactions.
Impact of AI on credit assessment and access to credit
AI has also arrived to transform credit assessment. Traditionally, credit scoring models rely on conventional parameters that may not fully reflect an individual’s creditworthiness.
Artificial intelligence can analyze a broader range of data, including non-financial information. This results in more accurate assessments with fewer errors. This approach can facilitate access to credit for people who, despite having poor credit histories, possess real repayment ability.
In other words, it gives opportunities to individuals previously deemed unfit for credit, benefiting many people who were believed to lack sufficient backing. This is related to financial inclusion and provides banks with greater assurances and peace of mind regarding potential customers.
Risks and challenges of AI in the financial sector
Despite its numerous advantages, the integration of AI into financial services also presents new risks. One major risk is cybersecurity. AI can increase the likelihood and complexity of cyberattacks, making techniques like phishing more sophisticated and malware more efficient. This is the flip side of the coin.
According to a McKinsey report, 60% of financial organizations have experienced an increase in the quantity and sophistication of cyberattacks over the past two years, and this trend is continuing. AI in the wrong hands can cause irreparable damage, not only to banks but also to any related individuals, clients, employees, or partner entities.
Additionally, AI models trained with biased data can perpetuate existing prejudices, affecting fairness in lending and insurance decisions. How is this possible?
Imagine a bank uses an AI model to decide whom to grant loans. This model is trained with historical data about past loans, such as income, age, and the applicant’s location.
Now, suppose that in the past, the bank approved more loans to people from certain areas of the city and was less generous with people from other areas due to historical biases or previous decisions. If the AI model is trained with this biased data, it will learn that certain areas are more “reliable” than others based on historical patterns.
As a result, the model might reject applications from less favored areas, even if they have strong financial situations, because that’s what the model has learned. Alternatively, it might show favoritism towards applicants from areas historically receiving more loan approvals, even if their financial situation is not as strong.
This could lead loan officers to make unfair decisions, perpetuating serious global issues such as existing inequalities. Therefore, it is essential to be very mindful of the context provided to the model to ensure fair decision-making.
The role of Central Banks in the age of AI
Central banks are particularly attentive to the impact of AI and have actively incorporated it to achieve their goals. Today, AI helps these institutions in data collection, statistical analysis, and payment system management.
Global initiatives have shown excellent results, confirming AI’s impressive capability to deliver positive outcomes. This technology significantly contributes to combating money laundering and improving payment network efficiency through techniques such as tokenization and advanced algorithms.
Central banks are also exploring the use of AI to strengthen cybersecurity. The ability of AI to automate repetitive processes and enhance threat detection is crucial in protecting financial structures against emerging cyber threats.
Last year, cyberattacks targeting financial institutions increased by 30% compared to the previous year. This is a concerning figure that continues to rise. Additionally, the global cost of data breaches in the banking sector reached $24 billion.
To put this into perspective, it is equivalent to the GDP of countries like Jordan or Malta, or about twice what NASA spends annually on its entire research and development.
AI can be crucial in this context. It has the ability to identify suspicious patterns and respond to threats in real-time, helping to reduce the risk of attacks and mitigate the potential impact of security breaches.
AI is a key player in the sector
In conclusion, AI is transforming the financial sector by improving efficiency, reducing costs, and offering new capabilities in credit assessment.
However, it is crucial to manage the associated challenges, such as cybersecurity risks and data biases. Central banks and financial institutions must adopt careful practices to maximize the benefits of AI and protect customers.
As guardians of financial and economic stability, they have the responsibility to guide AI integration towards positive outcomes for the global financial sector. They must ensure that the ultimate beneficiaries of this process are the users, who ultimately contribute to the corporate stability of banks and, consequently, to their growth.
At Interfaz, we bring extensive experience and expertise in the banking sector to help navigate these complexities. As a technology company with a deep understanding of financial services, we are committed to delivering innovative solutions that drive efficiency and growth.
