An Introduction to artificial intelligence in retail banking
The banking sector is a central part of the global economy and right now the advancements in Artificial Intelligence (AI) are playing a huge part in the dynamics of the industry.
Retail banking, in particular, is undergoing a massive period of transformation that's going to both enhance operational efficiency but also redefine and re-imagine the customer experience. This rapid adoption of AI technologies, from machine learning, generative AI to natural language processing, means that truly personalised banking services and experiences are to become the norm, rather than the exception.
At Beyond: Putting Data to Work, we have been harnessing AI in digital banking for many years with our AI solutions across both the customer behavioural and marketing angles as well as optimising clients Anti Money Laundering systems. Our industry expertise enables us to guide clients through the complexities of AI adoption and helping them put their data to work.
This blog post by Beyond, explores the potential impact of AI in digital banking, and outlines how things like generative AI, AI-powered fraud detection, and chatbots are no longer concepts but happening today and beginning to reshape the banking landscape.
Challenges in Digital Retail Banking
The Digital banking sector is currently experiencing a number of significant challenges, which have been driven by both external global or macroeconomic conditions and internal industry dynamics. These issues include:
Macroeconomic Pressures - higher interest rates and inflation, which affect everything from borrowing costs to customer savings behaviour demand a careful balance in terms of asset and liability management to maintain profitability and customer satisfaction.
Technological and Digital Transformation: as industry competition increases, there is a growing pressure to enhance digital capabilities to meet customer expectations for more personalised and seamless experiences. This is driving the increased use of advanced data analytics and artificial intelligence in particular to transform operations and customer service.
Regulatory and Compliance Requirements: With the advent of new technologies, increased scrutiny is coming from the regulators, especially with regards digital finance solutions. This is making transformation more complex as banks need to navigate additional regulatory complexities.
Cybersecurity and Data Privacy: With the increase of digital banking, cybersecurity threats have become more and more prolific meaning the banks need to protect their systems even more to protect data privacy and maintain trust with their customers.
Customer Centricity and Retention: New non financial entrants into the sector are shaking things up and bringing in new ways of thinking about the customer experience. Getting closer to the customer through data driven insights has become a much bigger priority as a result.
Artificial intelligence (AI) offers huge potential for banks to go some way in addressing these challenges. It can significantly enhance decision-making processes, scale up and improve risk management, and deliver hyper personalised customer service. AI enables the banks to analyse huge volumes of data at pace to gain insights into customer behaviour, predict future trends, and tailor products and services to individual needs.
The Evolution of Artificial Intelligence (AI) in Digital Retail Banking
AI in banking has been a gradual but impactful evolution. From its beginning, where relatively simple systems performed basic data processing, AI has matured into a technology or capability that is expected to drive the banking sector into a new era of efficiency and customisation. This transformation is a result of machine learning and natural language processing technologies that have allowed banks to finally harness their vast datasets and deriving insights that, due to legacy systems and other challenges, were previously deemed unattainable.
The potential for AI to add value to the banking industry is predicted to be massive. McKinsey's forecasts suggest AI technologies could unlock up to $1 trillion in additional value annually. This evolution is not merely about automation or operational efficiency; it's about leveraging AI to really understand customer needs, better anticipate their preferences, and ultimately deliver services that are not just responsive but predictive.
Generative AI, for instance, is enabling innovative product development and ways to deliver new types of customer interaction that were once the domain of fantasy. The ability to generate new content, ideas, or data patterns has huge applications, from personalising financial advice to creating more engaging customer service experiences. The further you look into the capabilities of AI and machine learning in banking, it becomes clear that the future of banking is going to be in the banks ability to to seamlessly integrate these technologies to offer services that are not only efficient to run but at the same time super-tailored to the individual customer's journey.
Generative AI in Banking
Unlike traditional AI, which interprets and acts on existing data, Generative AI can create new data and content. It does this by simulating human-like creativity and innovation which means creates a whole new relationship with technology. This leap from analytical to what could be called "creative AI" opens up a raft of new opportunities for banks to innovate.
At Beyond we see these falling into three broad categories:
Product Development: Generative AI can analyse trends and customer feedback to provide insights for new financial products, services or experiences. Its ability to support hyper-personalisation and targeting, it can tailor offerings to meet changing customer needs with an amazing level of precision.
Personalised Customer Experiences: In the same vein as generating customised content and solutions, banks can also use the same technologies and approaches to really get under the skin of each and every customer. By understanding and intuiting insights through customer behaviours allows the bank to offer advance customer segmentation and a level of personalisation that goes beyond standard segmentation, directly speaking to individual customer preferences and life moments.
Risk Management and Compliance: In areas where new scenarios or data are sparse, Generative AI through predictive modelling can simulate various outcomes to better prepare banks for potential risks, ensuring more robust compliance strategies.
Generative AI's capability to support innovation and personalisation is starting to transform the banking landscape by making what was once deemed too hard to deliver at scale, suddenly very achievable.
AI and ML in Banking
AI for Personalised Banking Experiences
The integration of AI and Machine Learning (ML) in banking has been a game-changer in delivering personalised services. By analysing vast amounts of customer data, AI and Machine Learning (ML) can uncover insights into individual preferences, spending habits, and financial goals, enabling banks to offer highly personalised banking experiences. The possibilities are endless but a few examples that are gaining traction include:
Customised Financial Advice: Leveraging AI, banks can provide tailored financial advice or guidance, helping customers help themselves to make informed decisions that align with their personal financial objectives.
Targeted Product Recommendations: AI algorithms can predict which banking products or services a customer is most likely to need, based on their transaction history and interactions, enhancing cross-selling and upselling strategies.
Enhanced Customer Support: Through tools such as natural language processing, AI banking bots offer a convenient new interface for many customer service requirements. This means real-time, 24/7 customer support, handling inquiries and transactions with a level of personalisation that is as good as, and sometimes better, than regular human interaction. Consider some of the challenges the older generations have faced accessing digital services provided by their banks. Generative AI enables a much more realistic or lifelike experience.
AI for Operational Efficiency in Digital Banking
Beyond personalisation, AI and ML significantly enhance operational efficiency within banks, automating routine tasks, and improving decision-making processes.
Automated Customer Service: AI banking bots and virtual assistants automate customer service tasks, from balance inquiries to transaction processing, freeing human agents to focus on more complex issues.
Fraud Detection and Prevention: AI systems excel at identifying patterns indicative of fraudulent activity, providing real-time alerts and reducing false positives, thus enhancing the security of customer transactions.
Streamlined Operations: From loan processing to risk assessment, AI automates and optimises numerous back-office functions, reducing costs and improving service delivery times.
The integration of AI and ML into banking operations is not just about technological advancement but a strategic imperative to meet customer expectations for personalised, efficient, and secure banking services.
AI for Fraud Detection in Banking
In the digital age, the growing sophistication of financial fraud tactics is posing an ever-growing threat to the security of our banking transactions and institutions. A day doesn't go by when each and every one of us isn't receiving some kind of attempt at cracking into our emails or bank accounts. AI is thankfully at the coal face of combating this menace, providing tools that are not only reactive but also proactive in detecting and preventing fraud. It's ability to do stuff at scale is starting to give the criminal fraternity a run for their money.
Pattern Recognition: AI algorithms are brilliant at sifting through millions of transactions in real-time, identifying patterns and anomalies that may indicate fraudulent activity is taking place, a task way beyond human capability for speed and accuracy.
Predictive Analytics: Beyond identifying current fraud, AI can leverage historical data to predict and prevent future fraudulent activities, adapting to new tactics as they evolve.
Customer Behaviour Analysis: By understanding the normal patterns of behaviour of a customer, AI tools can use this to model and detect deviations that suggest something is untoward, flagging suspicious activities for further investigation.
The implementation of AI in fraud detection is not only enhancing the security of banking operations but its fast becoming an important tool to improve customer trust, which is a critical asset in the financial sector.
Real-World Examples
The theoretical capabilities of AI in banking are impressive, but real-world applications are where the rubber hits the road and its transformative potential becomes evident.
At Beyond we have been using AI with our Banking clients for many years. This first really kicked off in the early 200's with our collaboration with Visa. Our mission was to transform their huge debit and credit card data sets into powerful consumer and market insights enabling a whole new merchant offers led loyalty scheme. AI was a critical player across this work, from helping clean and anonymise the data, create dynamic behavioural profiles of consumers lifestyles and sector based habits through to powering the automated and highly targeted offer recommendations appearing through individuals bank statements.
You can read the case study here: https://www.puttingdatatowork.com/beyond-case-studies/data-driven-innovation-at-visa
Here are a few other examples of how AI is being leveraged in the banking sector:
Customer Service Enhancement: Banks like Bank of America with its chatbot Erica, have introduced AI-powered banking bots that handle millions of customer inquiries, from transaction queries to basic banking advice, offering personalised and instant responses. The ability to mimic human like interactions is closing the gap between traditional technologies and soltuions that were deemed impersonal and cold. Unlike humans, AI chatbots are happily available around the clock, ensuring customers have access to banking services and support anytime, anywhere that they need.
Next Best Offer by Deutsche Bank: This AI-driven solution analyses DBs Wealth Management Clients' portfolios to build a profile of their customers finances and behaviours and use this to identify possible risks and recommend personalised financial products. This allows them to enhance and scale their current levels of service and investment strategy advice to meet an ever more demanding customer base.
Black Forest - Combating Financial Crime: Likewise Deutsche Bank's AI model, Black Forest, analyses transactions to detect suspicious activities, significantly improving the bank's ability to fight financial crime. By learning from each case, the AI system continuously enhances its accuracy and effectiveness.
These examples underscore the practical impact of AI in banking, from improving customer interactions to enhancing security measures and operational efficiency.
Challenges and Considerations for Consumer Banks Implementing AI
While AI presents huge opportunities for the banking sector, its implementation is not without challenges. Banks need to navigate a landscape of technical, regulatory, and operational hurdles to fully leverage AI technologies.
Some of the big things you need to be thinking about as you embark on your AI journey include:
Data Security and Privacy: As AI systems require access to vast amounts of customer data, ensuring the security and privacy of this data is super important. Banks must adhere to stringent data protection regulations, such as GDPR, complicating AI deployments. If you don't get this right your project will never even get of the ground as you can be sure your Governance and Compliance teams will put the brakes on any endeavours.
Integration with Legacy Systems: Many banks, other than the new market entrants, still operate on pretty outdated technology infrastructure and systems. Integrating advanced AI solutions with these legacy systems poses significant technical challenges and general data headaches. This can not only slow things down, but given AI relies on good quality data at volume , these legacy systems can significantly impede the adoption of AI technologies.
Skill Gaps and Talent Acquisition: The specialised nature of AI and ML technologies requires building a workforce with a specific set of skills. The current market for AI experts is extremely competitive, making it difficult for banks to attract and retain the necessary expertise. Banks are typically pretty corporate organisations and culturally may need to think about how they adapt their environments of provide different environments to suit these skill types.
Customer Trust and Adoption: Despite the advantages of AI-powered solutions, winning customer trust remains a big challenge. Concerns over data privacy and the impersonal nature of AI interactions can hinder customer adoption. Keeping in mind the demographics and wealth division of most of the leading economies is important. Some of the best, most valuable customers are, without over generalising, from older demographics with a much lower tolerance for the unknown or unproven as fas as data and technology are concerned.
Addressing these challenges requires a strategic approach, balancing innovation with security, privacy, and customer-centricity.
Conclusion and Future Outlook
AI's role in retail banking is looking to offer unparalleled opportunities for personalisation, efficiency, and security. From generative AI's potential to revolutionise product development and customer engagement to AI-driven analytics enhancing fraud detection and operational efficiency, the impact of AI technologies on the banking sector is undeniable and exciting.
To find out more about how Beyond can support you on your AI journey visit us at www.puttingdatatowork.com.