The financial world is experiencing a seismic shift, as artificial intelligence moves from buzzword to business essential. At the forefront of this transformation stands Bank of America's virtual assistant Erica – a powerful example of AI's real-world impact, having handled an astounding 800 million queries from 42 million customers while delivering over 1.2 billion pieces of financial insights.
This is just one of countless deployments that underlines AI’s role in transforming customer experience and boosting financial inclusion around the world - as well as driving greater competition as FinTechs get involved. It’s no surprise, therefore, that Gartner research indicates almost 60% of banking CIOs have deployed AI-based tools already, or are planning to do so within the next 12 months.
In this blog, we’ll explore how AI’s use in financial services is evolving, overcoming the challenges to navigate on the way, and highlighting the vital role it has to play in keeping sensitive financial data and funds safe.
AI use in financial services has grown rapidly, as financial institutions come to appreciate its ability to help them analyze data, make decisions, predict trends, and serve customers - and to do so faster, more cost-effectively and at vastly greater scale than was previously possible.
At the same time, many traditional banking players have also had to react to the aggressive disruption of new FinTech startups in the financial marketplace. Many of these new-generation firms have maximized their agility to create AI-supported, data-driven platforms, and have offered innovative solutions and services across personalization, risk assessment and customer experience, before established competitors have been able to.
Through 2025 and beyond, AI is increasingly being used for hyper-personalization, so that customers get the highly individual, on-demand service and support that they increasingly expect. And the deep insights it can help provide can drive new levels of efficiency and competitive advantage, especially over businesses that don’t have the same technologies.
However, there are many serious hurdles to navigate before that aspiration becomes a reality across the sector.
Even though the use of AI in the finance sector is in its relative infancy, with a huge amount of potential still lying downstream, a number of transformative benefits and practical use cases have already been realized:
AI algorithms can delve deeply into the finer points of an individual's creditworthiness, finances, behavior and banking activity: their income, how they spend it, how they pay off debt, what they like to invest in and so on. These insights enable banking firms to identify and offer the right financial products, at the right time, to the right customers, at the right price point - whether that's restructuring debt repayments or planning for retirement.
Additionally, AI has expanded the intelligence and capabilities of chatbots, enabling these virtual customer service assistants to deal with a greater range and complexity of queries. This has resonated well with customers: according to Pymnts, 72% of retail banking customers say they prefer intelligent virtual assistants to ‘traditional’ chatbots.
Machine learning is capable of detecting increasingly complex patterns within data, far beyond the capabilities of even the most skilled humans. This means that suspicious trends of transactions can be flagged up sooner, and potential cases of fraud can be addressed much faster. Given that 29% of American bank customers experienced fraud in the 12 months in 2024, this extra protection can make a big difference for millions of people worldwide.
The same principles can also be applied to assessing credit risk when customers apply for loans and mortgages, learning from historical data to protect the likelihood of default in the future. Additionally, AI algorithms can support Anti-Money Laundering (AML) operations, analyzing transactions to spot potential cases of financial crime efficiently and effectively.
Being able to use AI to automate repetitive tasks is saving banking firms huge amounts of time and money. Typical tasks that are already automatable include loan approval decisions, customer onboarding, account closing and payment collections. It’s also possible to automate large-scale data analysis and risk identification as part of compliance reporting and adherence with strict financial regulations.
Automation is now being extended to a wider and more advanced range of financial services. These include mortgage underwriting, where borrower risk is calculated through analysis of credit history, property value, income stability and other key metrics; and even stock market trading, where insights into past price movements, economic variables and market sentiment can help traders make the right calls at the right times.
As AI is an emerging technology that’s evolving all the time, and as financial services is such a complex and heavily regulated industry, making the most of the opportunities available can be easier said than done. There are a number of major hurdles to navigate, including:
It’s essential that customer information and funds are protected, not only to mitigate the risk of cybercrime, but also to comply with data protection laws and to maintain consumer confidence. Achieving this requires a high quality of data, without missing values, duplicates and errors; strong security to defend against unauthorized access and breaches; and clear governance polices and procedures around data access and use.
The rise of AI means banking firms have a whole new set of regulations and requirements to comply with, on top of the already stringent rules that apply to their industry. This demands robust governance frameworks, fully documented decision-making and regular audits. As AI has the power to transform lives and society, this level of regulatory care and oversight is key for legal, trust and reputational reasons.
Connected to the previous point, there is increasing societal expectation for AI to be used transparently and ethically, and to make positive contributions to the world rather than negative ones. This means banking firms using AI have to be upfront about what they are using AI for, how data is being applied to AI algorithms, and what this means for this customer. This will form the backbone of ‘responsible AI’, which is likely to become more and more of a global priority in the months and years ahead.
IBS Intelligence has found that 55% of banks feel their core banking systems are the biggest obstacle between them and their digital objectives. However, new technologies like AI need careful integration with legacy systems, which can be complicated, time-consuming, expensive, and can risk disruption to important banking systems during the change. Global spending by banks on outdated payment systems could reach $57 billion by 2028, so integration that enables compliant and ethical data extraction is becoming essential.
Banking organizations may also find that they come across a number of other technical stumbling blocks along their AI journey. These include incompatibility between legacy systems and advanced AI technologies (which mean expensive infrastructure overhauls are required); a lack of scalability in existing systems to handle the increased demands of AI workloads; and siloed data that is difficult to access and integrate for AI purposes.
When used for financial decision-making, there can be risks of AI algorithms unintentionally building biases into decision-making, which can advantage or disadvantage different types of people unfairly. These actions go against the Environmental, Social and Governance (ESG) commitments that most major organizations make, and so ensuring AI predictions are both accurate and fair is important for equality and trust.
It’s worth considering that while AI can be used as a force for good in the world of finance, this is not always the case.
Sophisticated cybercriminal operations can use AI to develop complex, adaptive attacks that target network vulnerabilities; overwhelm traditional security defences at speed; and develop personalized phishing scams that can fool even employees and customers who are security-conscious. It has also been known for financial firms to be affected by data poisoning (altering training data to deliberately generate biased results), or have sensitive AI model training data stolen.
These risks emphasize the need to secure AI tools used in financial services. This security should combine:
Alongside these measures, AI itself can also be used as a further means of defence. As mentioned above, it can be a highly useful tool in fraud prevention, spotting patterns of irregular activity and allowing remedial actions and responses to be taken much faster, so that any potential impact is limited.
Additionally, AI security systems can automate actions like dynamic security checks based on user behavior, blocking IP addresses thought to be malicious, or isolating systems affected by a breach. Cutting down the lead time between threat detection and response like this can also play a major part in minimizing the damage caused by an attack.
While none of the challenges mentioned above are insurmountable - and the benefits of AI for financial services are clear - organizations may still face some internal resistance. Risk-averse staff, deeply ingrained legacy systems and concerns about AI ethics may all be presented as barriers to progress.
This is where strategies for fostering a culture of innovation are so important. In particular, a combination of leadership training, incentive programs, and cross-functional collaboration can drive technological transformation.
And this is where a third-party finance AI expert like Ciklum can help. We have a proven track record in helping banks scale AI solutions for long-term success, including:
Ready to transform your financial services with AI? Contact Ciklum today to discover how our expertise in AI implementation can drive your competitive advantage and operational efficiency.