Struggling with slow service and growing customer complaints? Discover how AI is revolutionizing the banking industry and why it could be the key to staying ahead of your competitors.
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Picture this: Customer complaints are piling up in your financial organization. Your competitors are pulling ahead, offering faster services. You know something needs to change, but what? The answer might just lie in the transformative power of artificial intelligence. Investing in AI can be a much better idea than hiring new staff and hoping they will be able to handle your challenges, especially when it comes to repetitive tasks.
This article takes a closer look at the unprecedented potential of AI in banking. We discuss how algorithms can support efforts toward enhanced efficiency, personalization, and overall growth for numerous financial institutions.
The impact of AI and Generative AI in banking is significant. According to BCG, banks can use generative AI to reduce inquiry costs by ten times, cut the time spent on marketing content creation by 25%, and increase content creation productivity by 30%. Additionally, it improves customer satisfaction and speeds up issue resolution.
Now, let’s explore some key use cases of AI in banks and financial organizations. Which operations are most commonly automated and improved through AI?
Gone are the days of lengthy hold times and frustrating automated phone systems. AI technologies, including AI-driven chatbots and virtual assistants, are significantly changing customer service in banking. Their capabilities have become so advanced that it’s harder than ever to tell the difference between bots and human representatives.
According to the State of AI in Banking report, 48% of banks use chatbots and virtual assistants for customer interactions. The main benefit of using AI in customer service is 24/7 availability. Customers can get instant responses to queries at any time, and their interactions with bots are personalized thanks to data analytics and customer data that allow algorithms to provide tailored advice and solutions. AI is also capable of efficient query resolution - complex issues are seamlessly forwarded to human agents when necessary.
Generative AI in banking uses algorithms to turn money-related companies into personal financial advisors. They can provide customized investment strategies based on individual risk profiles and financial goals. Moreover, financial companies can offer insights and recommendations for better money management thanks to spending pattern analysis.
With AI-driven, tailored alerts about potential future issues or opportunities, customers can proactively plan their finances, such as investments and savings. By understanding individual spending habits and financial goals, AI can tailor services to each customer, fostering loyalty and engagement.
AI models are transforming how banks assess creditworthiness. That’s mostly because AI can harness algorithms to analyze data from alternative sources like social media or past spending patterns. Non-traditional factors can help prepare better loan offers or help customers postpone decisions that might hurt them in the long run.
Moreover, thanks to real-time updates, financial institutions can continuously adjust credit scores based on the latest financial behaviors and inform customers when they’re ready for a loan. Algorithms can also minimize human biases in lending decisions, which is crucial for ensuring an equal customer experience.
No wonder 62% of surveyed banks believe AI will add significant value to credit scoring in the next 3-5 years.
When it comes to combating financial fraud and other cyber threats, machine learning and AI offer a plethora of tools to handle these issues. Pattern recognition proves to be handy in identifying unusual transactions in real-time. Behavioral biometrics can analyze typing patterns, mouse movements, and other activities to detect unusual actions within banking systems.
AI is capable of adaptive learning, continuously improving fraud detection models based on new data. This not only increases the operational efficiency of cybersecurity measures but also significantly enhances the overall safety of personal data, customer accounts, and internal files.
AI solutions can streamline back-office operations. For example, using optical character recognition (OCR) and natural language processing (NLP) allows algorithms to automatically process documents, categorize them, and speed up information searches.
Optimizing task allocation and reducing processing times can be achieved with AI-powered workflow management that automates either entire processes or parts of them. Furthermore, with predictive maintenance algorithms, companies can anticipate issues before they cause disruptions and fix them faster than ever. Generative AI in banking proves itself useful for marketing and sales automation, too.
40% of banks surveyed by the Harris Poll admit to using AI in predictive modeling. Turning raw data into actionable insights is AI’s specialty. It can analyze vast amounts of data to forecast market movements and predict trends, helping prepare new offers and investment strategies.
Identifying key customer groups for targeted marketing and product development is another task AI can manage to help banks increase profit. The same goes for risk assessment, which can prevent financial companies from making poor decisions.
Find out what are the top 6 generative AI trends at the moment HERE.
To understand the impact of artificial intelligence on the banking sector in various areas, it’s essential to learn about real-world use cases. Here are our picks:
JPMorgan Chase developed COiN (Contract Intelligence), an AI-powered platform that automatically reviews commercial loan agreements. Efficient data collection and analysis are crucial in managing the vast amounts of data generated from daily transactions, enhancing user experiences, detecting fraud, and making informed credit decisions. The results were spectacular:
Financial service providers, such as Bank of America, have been leveraging AI technologies to enhance customer experiences and operational efficiency. Bank of America’s AI-powered virtual assistant, Erica, has been a game-changer for the company and is an example of a solution that many market players treat as a role model.
HSBC partnered with the AI firm Quantexa to enhance its risk management, anti-money laundering (AML), and fraud detection capabilities. This comprehensive system not only saves resources for the bank but also improves customer experience and safety.
According to the McKinsey Global Institute (MGI), generative AI could contribute between $200 billion and $340 billion in annual value to the global banking sector, accounting for 2.8% to 4.7% of total industry revenue, primarily by boosting productivity. However, as banks and financial institutions rush to adopt the technology, they are encountering various challenges. Let’s look at them now.
Financial services companies have to find a balance between personalization and data protection when introducing AI. That’s why ensuring compliance with regulations like GDPR and CCPA should be high on the priority list.
To give you a sense of the complexities, let’s take the US AI in the banking compliance landscape. As John Beck, attorney and Founding Partner at Beck & Beck Missouri Lawyers explained to us, “the banking sector operates under layers of federal and state laws, including the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the Bank Secrecy Act (BSA).” As Beck explains, AI models making lending or risk assessment decisions must adhere to anti-discrimination laws, and if they introduce bias - intentional or not - banks can face lawsuits and regulatory scrutiny. “I’ve seen cases where banks were flagged for discriminatory lending patterns because their AI models, trained on historical data, disproportionately denied loans to certain demographics”, he said. “Even if unintentional, banks are still legally responsible for AI bias, which has led to multiple financial institutions facing legal claims and settlement agreements”.
There’s also the case of building and maintaining customer trust while using AI systems. It requires transparent communication and step-by-step implementation in line with legal regulations and financial industry guidelines.
Firdaus Sateem, Founder at VoidSEO, told me that the main difficulty which banks encounter when using AI stems from problems related to data quality and access control. AI systems deliver effective operations when they receive precise and well-organized data that is free from errors. The majority of banks continue to work with outmoded technological systems that maintain unconnected data sources. Weak data quality combined with inconsistent and untidy record storage creates multiple sources of information that remains inaccessible to other systems.
“My work with AI models shows that minor data quality problems make the systems perform inadequately and generate weak outcomes. The fear of internal data exchange across departments because of privacy or security considerations leads banks to delay data sharing among departments. The absence of barrier removal will stop AI projects from generating valuable results,” said Sateem.
Many banks, especially those that operated long before the online era, struggle with digital transformation. Their main issue is innovating outdated IT infrastructure. This is, arguably, the most common challenge for the whole industry. IBS Intelligence reports that, as of late-2024, more than 55% of banks said their innovation and reaching new business goals were blocked by incompatibility of legacy systems.
“Banking systems are full of inconsistencies, incomplete records and redundancies. These types of structures make it difficult to consolidate and prepare for advanced AI applications”, says Mitchell Cookson, Co-Founder of AI Tools Inc.
Cookson says that many banks store customer data in separate systems – for mortgage accounts, saving accounts, credit cards, etc. – without a unified view.
“Organizing the disparate datasets into one, cohesive framework for AI implementation can be technically complex and resource-intensive. Moreover, poor data quality will automatically lead to inaccurate AI predictions. But all hope is not lost.”
To ensure seamless data flow between new AI systems and existing platforms, financial organizations must invest in legacy software updates and carefully plan how the algorithms will be integrated into the existing ecosystem.
As Cookson told us, they should also focus on data cleansing, deduplication, and real-time synchronization to make their data AI-ready.
AI algorithms can be biased, so it’s important to address potential mistakes of that nature. This can be done by providing proper training for the AI model with diverse and accurate data. Other ethical considerations involve AI-driven decision-making processes that should be highly transparent and always double-checked by humans. The same goes for critical financial operations that should always be controlled.
Training existing staff to work alongside AI systems should be an integral part of every modern bank’s strategy. If a financial institution wants to take full advantage of artificial intelligence, it should also focus on hiring and retaining AI and data science talent. The cultural shift towards an AI-driven organization is an ongoing process, but with the right attitude from leaders and managers, it’s achievable and beneficial.
Nathan Barz, Founder and CEO at DocVA, and an ex Financial Advisor at Barclays, told us that “finding true artisans with the relevant knowledge to engineer, deploy, and maintain AI systems was an endemic struggle. These experiences not only presented me with challenges but also provided me with perspectives on the solutions”.
He added that addressing these challenges will take much more than technology; it will take a proactive, adaptive culture, investment in people through training, and partnerships prioritized on innovation. It’s a tall order, though an extremely fulfilling one when executed properly.
AI is moving beyond basic personalization to hyper-personalization, where every customer interaction is tailored in real-time based on a deep understanding of individual preferences, behaviors, and financial goals. These mechanisms, already prolific in industries like retail, are now also making waves in banking.
As Luxembourg’s PwC division aptly noticed, 73% of consumers in a survey they ran said hyper-personalization matters in making purchasing decisions. These decision-making factors, as the consulting brand argues, also apply to banking.
From a technical standpoint, implementing it involves analyzing vast amounts of data to offer highly customized products, services, and advice that feel uniquely tailored to each customer. On the flip side, however, the regulatory landscape in financial services requires a strict approach. In an interview for PYMTS, i2c CEO and chairman Amir Wain said that: “because of the quality [and regulatory] requirements, the financial services sector should use an augmented AI approach”. Compliance must always take the first seat.
With the rise of voice assistants like Amazon Alexa and Google Assistant, voice-activated banking is becoming more common. According to Research and Markets, the voice banking market is set to grow from $1.61 billion in 2024 to $2.99 billion by 2028, picturing a promising outlook for the technology.
The future of AI in banking is anticipated to be bright and full of opportunities. This means business professionals in finance have to embrace it — or risk being left behind. To start this transformation, it’s important to assess the current state of the company and identify areas where AI can make the most impact. We believe that starting small (i.e., using AI across pilot projects that can demonstrate quick wins) is the best approach to building momentum and introducing significant changes.
It’s also critical to invest in the right staff and form partnerships with providers that can make AI implementation successful. Aligning such changes with business goals, internal systems, and potential resistance requires skilled experts who will not only deliver high-quality tools but also keep the organization’s needs in mind.
Check out the picks of companies that specialize in software development for financial institutions. Working with them means your unique needs will be met, and the final result of the collaboration will address the specific challenges of your brand and its customers.
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Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.
A serial entrepreneur, passionate R&D engineer, with 15 years of experience in the tech industry. Shares his expert knowledge about tech, startups, business development, and market analysis.
Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.
A serial entrepreneur, passionate R&D engineer, with 15 years of experience in the tech industry. Shares his expert knowledge about tech, startups, business development, and market analysis.
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