When it comes to technological innovations, the banking sector is always among the first to adopt and benefit from cutting-edge technology. The same holds for generative artificial intelligence (Gen AI), the deep-learning technology that can generate human-like text, images, videos, and audio, and even synthesize data for training other AI models. Formerly limited to physical establishments, banking has morphed into a completely digital realm, due in no small part to generative AI.
Interest in Gen AI solutions has been sky-high in the sector, and the future trajectory of generative AI in banking is set to soar even higher.
According to McKinsey research, banking (alongside high tech and life sciences) is among the industries that are expected to get the most out of generative AI as the percentage of their revenues – the total potential added value delivered by the technology could range between $200 billion and $340 billion annually.
The integration of generative AI into banking operations foreshadows a seismic shift in the landscape. For banks, the question now is not whether generative AI will significantly impact the banking sector but rather how. How is generative AI poised to change the traditional banking paradigm?
In this blog post, we aim to unravel the transformative potential of the novel technology in banking by delving into the practical application of generative AI in the banking industry. As we continue our exploration, we will highlight the potential Gen AI adoption barriers and offer some key fundamentals to focus on for its successful implementation.
Generative AI in banking: current state of affairs
Before we dive into Gen AI applications in the banking industry, let’s see how the sector has been gradually adopting artificial intelligence over the years.
The evolution of AI in banking
Given the nature of their business models, it is no wonder banks were early adopters of artificial intelligence. Over the years, AI in baking has undergone a dramatic transformation since machine learning and deep learning technologies (so-called traditional AI) were first introduced into the banking sector. With the release of Python for Data Analysis, or pandas, in the late 2000s, the use of machine learning in banking gained momentum. Banking and finance emerged as some of the most active users of this earlier AI, which paved the way for new developments in ML and related technologies.
Traditional AI systems in banking primarily rely on machine learning. They use the technology to recognize patterns in historical data to identify root causes of past events or define trends for the future. Such systems use predefined rules and are trained on structured data often stored in databases and spreadsheets.
Common use cases of traditional AI systems in banking include:
- Fraud detection
- Customer service automation
- Credit score calculations and risk assessment
- Algorithmic trading
- Market trend and customer behavior prediction
Each successive FinTech innovation that came along incrementally transformed banking across its multiple functions, one by one, until generative AI entered the scene to drastically reinvent the entire industry.
Gen AI to reshape banking business models
The advent of generative AI in the banking industry is not about technology evolution – generative artificial intelligence is set to redefine the very essence of banking by shaping entirely new business models. The impact Gen AI has on the banking sector is immense across literally all banking functions, especially in terms of banking operations and decision-making. In the data-rich banking environment, where customer interaction plays a critical role and a substantial workforce performs a wide range of daily routine tasks, generative AI emerges as a catalyst for redefining the boundaries of operational efficiency, customer experience, and rule-based decision-making.
While traditional AI has come a long way in improving efficiency and decision-making in the banking sector, it may have limitations when dealing with unstructured data, natural language understanding, and complex contextual analysis. Generative AI technologies provide a range of state-of-the-art capabilities that have the potential to address these limitations and go even further.
- Compared to conventional AI, Gen AI’s large language models (LLMs) can learn patterns and structure from even larger volumes of data, including information from unstructured inputs. In banking, this represents a shift towards more sophisticated and creative AI models (such as GPT, Generative Pre-trained Transformer) that can generate highly personalized content for communicating with customers.
- While traditional AI simply analyzes data and makes predictions by following pre-programmed rules, generative AI, built upon deep neural networks, autonomously creates coherent and contextually relevant outputs.
- Gen AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large language models like GPT can create synthetic data that replicates the statistical properties of real-world datasets. This is particularly important when real data is scarce, expensive, or sensitive. A practical example could be generating transactional data for anti-fraud models.
Overall, the switch from traditional AI to generative AI in banking shows a move toward more flexible and human-like AI systems that can understand and generate natural-language text while taking context into account. This is instrumental in creating the most valuable use cases in both customer service and back-office roles. In banking, this can mean using generative AI to streamline customer support, automate report generation, perform sentiment analysis of unstructured text data, and even generate personalized financial advice based on customer interactions and preferences.
The most promising use cases for generative AI in banking
While some financial institutions are adopting generative AI tools at a breakneck pace (though mostly as pilot projects on a small scale), corporate implementation of Gen AI tools is still in its infancy. For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands.
So let us elaborate on how the traditional banking experience can be transformed into a highly differentiated, secure, and efficient service by the convergence of generative AI and banking. These most promising generative AI use cases in banking, with some real-life examples, demonstrate the potential value arising from the technology.
Customer service enhancement
- Advanced chatbots for 24/7 customer support
Due to their capability to produce meaningful text that resembles human-written content, AI-driven chatbots allow banks to provide 24/7 support, removing customers’ need to wait in long lines or navigate difficult phone menus. These smart virtual assistants act autonomously and can provide customers with on-the-spot guidance and prompt support by responding to basic customer requests, such as:
-Recommending financial services and banking products
-Showing deposit options
-Checking account balances and transaction history
-Initiating and completing transactions
A good example is Wells Fargo’s generative AI virtual assistant named Fargo. The assistant has reportedly handled 20 million interactions since it was launched in March 2023 and is poised to hit 100 million interactions annually. Using Google’s PaLM 2 LLM, the app is designed to answer customers’ everyday banking queries and execute tasks such as giving insight into spending patterns, checking credit scores, paying bills, and offering transaction details, among others.
- AI-driven personalized financial advice
Along with direct use by customers, generative AI-based chatbots can also significantly assist the front line by suggesting client-specific actions. By examining real-time customer interactions and transactions, Gen AI can offer insightful data on specific customer behaviors and preferences, which can then be used by financial advisors to offer more personalized user experiences.
Generative AI algorithms analyze vast customer data, including transaction history, account balances, spending patterns, investment portfolios, and financial goals, to build a comprehensive customer profile. This allows banks to enhance their service operations by offering hyper-personalized recommendations based on their specific circumstances, creating customized financial plans, and providing tailored financial advice and product suggestions.
For example, Morgan Stanley has launched an AI assistant based on OpenAI’s GPT-4 that allows its 16,000 financial advisors instant access to a database of about 100,000 research reports and documents. The AI model aims to help financial advisors quickly find and synthesize answers to investing and finance queries and offer highly personalized instant insights.
Data-driven decision-making
- Trend analysis for market and investment strategies
There is a rich potential for generative AI tools to considerably assist in strategic decision-making. For one, generative AI can analyze market trends, financial market data, economic indicators, and investment opportunities to generate personalized investment recommendations. Furthermore, it can synthesize and test different market scenarios to propose and evaluate the effectiveness of new trading strategies, thereby helping banks identify profitable opportunities and minimize losses.
While there’s been increasing interest in applying generative AI across these functions, banks are still exploring how generative AI could be used for generating market and investment strategies. According to Jason Napier, Head of European Banks Research at UBS, “While later there will be other, probably more important, deployments, a lot of the potential of AI appears really nascent at this stage.”
- Fraud detection and risk assessment
With generative AI on board, banks are well-equipped to enhance their fraud detection capabilities and improve risk assessment. Here is how Gen AI can help.
- With little help from human users, generative AI can promptly identify transaction anomalies indicating fraudulent activity, such as odd locations and devices or unusual spending patterns, and automatically flag potential hazards.
- What’s more, Gen AI techniques, such as GANs, can create synthetic fraudulent transactions to provide a more diverse set of scenarios for training fraud detection models. This can prove critical in improving the robustness and accuracy of fraud detection algorithms.
- Gen AI algorithms can provide insights into the underlying patterns contributing to fraud alerts, which enables more effective decision-making.
- Generative AI models can help banks identify possible risk areas and preserve profitability by analyzing historical data patterns and market trends. By simulating different economic scenarios, GANs can help banks assess and mitigate risks, such as credit risk, market risk, and operational risk.
Mastercard has recently announced the launch of a new generative AI model to enable banks to better detect suspicious transactions on its network. According to Mastercard, the technology is poised to help banks improve their fraud detection rate by 20%, with rates reaching as much as 300% in some cases. The 125 billion or so transactions that pass through the company’s card network annually provide the training data for the model.
Compliance and regulatory checks
- Automating KYC (Know Your Customer) verification processes
Just as banks and financial institutions need to verify the identity of their clients to avoid commercial relations with synthetic businesses or people related, for example, to fraud, corruption, or money laundering, it is equally critical for them to achieve requirements set by multiple KYC compliance regulations, such as AML, GDPR, and eIDAS.
Banks can use generative AI technology to automate the time-consuming process of customer due diligence by analyzing large amounts of customer personal data. This may help cut down on customer onboarding time, reduce false alarms, and enhance the accuracy of risk assessment while also ensuring compliance with stringent AML and KYC regulations.
For example, Airwallex, a global payments company, has introduced a generative AI copilot that utilizes large language models to accelerate the company’s KYC assessment and onboarding processes. The implementation of the Gen AI tool has reportedly reduced false-positive alerts by 50% during the KYC due diligence phase and sped up the KYC onboarding process by 20%. Previously, the company used rules-based analytics and NLP to scrutinize new customers’ websites, which often resulted in numerous false-positive alerts.
- Real-time monitoring and reporting for regulatory compliance
Regulatory compliance is another area to benefit from generative AI in the banking industry. Banking is a highly regulated industry – regulatory requirements are subject to frequent changes and updates. Keeping up with increasingly frequent regulatory changes puts a strain on banking and finance staff, as it requires a vast amount of manual and repetitive work to interpret new requirements and ensure alignment with regulatory standards.
Generative AI can help banks maintain ongoing compliance with ever-evolving regulatory requirements by continuously monitoring regulations in real time, identifying compliance risks, and generating accurate and timely reports.
For example, Gen AI has been recently employed by Citigroup to evaluate the effects of new US capital regulations. The bank’s risk and compliance team used generative AI to sift through and summarize 1,089 pages of new capital rules released by the federal regulators. Moreover, they are looking to use large language models to parse legislation and regulations in the countries they operate in to ensure they abide by regulations in each jurisdiction.
Cost optimization and process efficiency
- Automating routine tasks to reduce operational costs
One of the highest-value generative AI use cases in banking revolves around automating tedious activities that previously required human input. As an MIT Technology Review Insights report says, “Banks and insurance are among the industries with the greatest proportion of their workforces exposed to potential automation.” Since personnel expenses account for a sizable amount of total costs, the introduction of Gen AI automation into banking operations has the potential to substantially reduce operational costs. These cost savings mostly result from using Gen AI technology to take away the need for analyzing vast volumes of frequently unstructured data.
Owing to its enhanced ability to understand context and generate natural language texts, summarization capabilities, and predictive intelligence, generative AI holds promise to automate and streamline most of the back-office processes for greater operational efficiency. This will enable operations staff to focus on customers rather than crunching numbers. Accenture forecasts that by 2028, the banking industry will witness a 30% increase in employee productivity. Here is how generative AI can augment the back-office workforce:
- Accelerate report generation using gen AI tools to search and summarize volumes of data in unstructured documents
- Compress lengthy documents into summaries
- Speed up complicated post-trade processes in post-trade operations
- Process loan applications by analyzing various data points, including the applicant’s credit score, financial history, and current data
- Create summaries following business interactions or phone conversations
Several banks are already using generative AI to automate their routine tasks.
For example, Fujitsu and Hokuhoku Financial Group have launched joint trials to explore promising use cases for generative AI in banking operations. The companies envision using the technology to generate responses to internal inquiries, create and check various business documents, and build programs.
One more example is the OCBC bank, which has rolled out a generative AI chatbot for its 30,000 global employees to automate a wide range of time-consuming tasks, such as writing investment research reports and drafting customer responses. The staff had reported a 50% increase in productivity rate during the trial period.
Furthermore, Morgan Stanley is currently piloting another tool called Debrief, designed to create automatic summaries of client meetings, draft follow-up emails, and schedule follow-up appointments.
Not a magic wand so far: recognizing the challenges of generative AI for banking
While generative AI holds big promise for the banking industry, most of the current deployments are limited to just a few banking areas or don’t go beyond the experimental phase. Though early generative AI pilots appear rewarding and impressive, it will definitely take time to realize Gen AI’s full potential and appreciate its full impact on the banking industry. Banking and finance leaders must address significant challenges and concerns as they consider large-scale deployments. These include managing data privacy risks, navigating ethical considerations, tackling legacy tech challenges, and addressing skills gaps.
Here is a list of key considerations for generative AI in banking.
- Data privacy and regulatory concerns
- The potential benefits and opportunities generative AI offers to the financial sector are undeniable. However, the adoption of generative AI also raises data privacy and security concerns, which are major for the banking sector.
- First, there is always a risk of unintentional violation of customers’ privacy rights when collecting publicly available client data for profiling and forecasting. Gen AI can inadvertently reveal sensitive or personally identifiable information, such as personal identification details, transaction history, and account balances.
- Just as generative AI in banking continues to evolve, so do fraudsters, seeking new ways to exploit new technology for scaling their scams. For example, scammers might use Gen AI to create phishing and SMiShing attacks, fake browser extensions, or impersonation scams.
- Finally, the nature of generative AI is still largely unregulated. This poses a significant barrier to the large-scale adoption of generative AI in the banking industry. As the chief executive of the UK’s Financial Conduct Authority (FCA) said, “While the FCA does not regulate technology, we do regulate the effect on – and use of – tech in financial services…. With these developments [the growing use of generative AI], it is critical we do not lose sight of our duty to protect the most vulnerable and to safeguard financial inclusion and access.” While full regulation of AI by the government is under consideration so far, the potential value of an extensive application of generative AI should be balanced against regulatory risks.
The mitigation solution is to have robust cybersecurity measures in place to prevent hacking attempts and data breaches. As for regulatory compliance, Gen AI itself provides banking and finance with an efficient means of keeping abreast of changing regulatory environments.
Legacy systems
Legacy technology is another factor slowing down Gen AI’s commercial use. Such systems impede the integration of innovative capabilities that novel technologies deliver. First, they often use outdated data formats, structures, and protocols that may be incompatible with modern Gen AI technologies. Secondly, they may store data in siloed or proprietary formats, making it difficult to access and retrieve data for generative AI model training and analysis.
Interestingly, generative AI itself can serve as a solution to the legacy infrastructure problem by propelling the transition from legacy software and data storage, which previously seemed unreasonable or cost-prohibitive. Gen AI’s ability to generate code can further assist with the transformation.
Legacy modernization is a daunting challenge – it involves a lot of time, financial resources, and effort. A trusted financial software development company that knows the ropes can help smoothly transform the existing infrastructure while also providing end-to-end support in building a powerful Gen AI solution.
Ethical challenges of generative AI in banking
Among the biggest concerns for the banking sector is Gen AI’s propensity for biases and unfairness.
Key points to consider:
- The resulting outputs can be biased if the data used to train a Gen AI model is incomplete or insufficient. Algorithmic bias may lead to unfair and discriminating lending decisions for certain population groups.
- Since Gen AI models are complex and sophisticated, bank employees can have a hard time interpreting the output of AI algorithms, which leads to the inability to explain the reason behind a model’s decision to customers or regulators.
- Generative AI models tend to produce confident wrong answers, referred to as “hallucinations.” While looking hyper-realistic, these outcomes are entirely fictitious in fact, which is catastrophic if applied in banking.
To address these issues, it’s critical to integrate human expertise into Gen AI’s decision-making processes every step of the way. Such a human-in-the-loop approach is a sure-fire way to detect the model’s anomalies before they can impact the decision. Using generative AI to produce initial responses as a starting point and creating feedback loops can help the model reach 100% accuracy.
Managing change and talent shortage
The talent shortage is another barrier standing in the way of generative AI adoption in the banking sector. According to John Mileham, CTO at Betterment, “Currently, generative AI is so new that you can’t really hire a whole lot of experience.”
Integrating Gen AI into banking operations will certainly reshape many roles in the banking workforce in that workers will have to learn new skills or change occupations.
To bridge the skills gap, financial services firms will have to figure out what new competencies and skills the workforce will have to acquire and whether they need to reskill and upskill existing employees or hire new ones. This will require extensive investments in retraining and hiring initiatives to meet changing talent needs. Providing internal training programs for employees is key to generating excitement and equipping your existing teams with the resources, skills, and capabilities required for the new roles, such as prompt engineering or model fine-tuning skills.
Key foundations of generative AI implementation in banking
The integration of generative AI solutions into banking operations requires strategic planning and consideration.
Here we give the essential tips to help you lay the right foundations for an effective Gen AI implementation strategy.
1. Define priority areas and set goals
First and foremost, as with any new technology, banks need to have a clear use case to align their efforts to business impact – i.e., to be clear about why they need generative AI:
- Specify priority areas (functions or units) to experience the biggest impact from generative AI technology and plan for specific use cases (frontline copilot, customer operations, or discovery of regulatory changes, to name a few)
- Clearly define the objective and outcomes
- Examine the interoperability of your current data infrastructure with generative AI tools, assess skills, and evaluate data and technology
2. Optimize infrastructure
Think about modern infrastructure and systems capable of supporting Gen AI technologies. A good option would be hybrid infrastructure, which allows banks to work with private models for sensitive data while also leveraging the public cloud capabilities.
3. Pilot the technology
Start with a pilot project to evaluate its feasibility, analyze its potential risks, and measure the adoption. Train, deploy, and test the generative AI system on a small scale before expanding it to critical use cases like loan underwriting or generating investment strategies. Once this is done, you should be able to answer the following question: Is the system ready for enterprise-changing generative AI initiatives?
4. Establish strong controls
Given that generative AI brings new risks to the banking industry, banks and financial institutions will need to design new AI governance frameworks and control sets from the very outset, both for internal use cases and third-party tools, to promote the responsible use of the technology. This refers both to unregulated processes such as customer service and heavily regulated operations such as credit risk scoring.
These are key essentials you may want to focus on for a successful Gen AI implementation strategy. To establish a solid foundation for building robust generative AI solutions, banks need a comprehensive implementation roadmap to include yet more strategic steps. As a highly experienced generative AI company, ITRex can help you define the opportunities within your business and the sector for generative AI adoption.
In closing
The transition to more advanced generative AI models represents a shift towards addressing the challenges traditional AI systems can’t grapple with. Generative AI use cases and applications in the banking sector grow daily. Some banks have already embraced its immense impact by applying Gen AI to a variety of use cases across their multiple functions. This includes lower costs, personalized user experiences, and enhanced operational efficiency, to name a few.
However, other banks are predominantly making their first steps toward new frontiers Gen AI opens, simply testing the waters, with the practical application of generative AI in banking being mostly reduced to automating low-value routine tasks and workflows that previously required a human. But that’s only a starting point. There has never been a better time to seize the chance and gain a competitive edge while large-scale deployments remain nascent.
How banks will leverage generative AI still holds surprises. But one thing is clear as banks navigate this new realm: generative AI is shaping the future of banking.
Let’s shape the future together!
Looking to boost productivity in the front and back lines? No problem. Want to enhance risk assessment or streamline regulatory compliance? ITRex can do that too. Reach out to our AI experts for a tailored generative AI solution for banking.
The post Generative AI in Banking – Use Cases & Challenges appeared first on Datafloq.
