Sean Hennigar
, December 3, 2024
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Successful integration of emerging technologies has always been key to fostering innovation and staying competitive in financial services. Generative AI (gen AI) has emerged as a promising new technology due to its ability to generate dynamic content and provide a natural and personalized user experience. Recent industry surveys indicate that 60% of US banks plan to use generative AI to bridge talent gaps and automate up to 20% of daily tasks.1  While the financial services sector has shown great interest in gen AI, many organizations are still in the early stages of evaluating the technology to determine its optimal utility and understand its specific limitations and risks.

In this blog, we explore how gen AI can be seamlessly integrated into your financial services organization’s artificial intelligence strategy to achieve business goals. Gen AI presents new opportunities to improve productivity, drive operational efficiency, and enhance the customer experience; but it also poses unique challenges that organizations must prepare for. Read on to learn how gen AI can complement your technology strategy and improve business outcomes.

The role of advanced technologies in transforming financial services 

The financial services industry has always led in the early adoption of new technology.  In recent years, advanced analytics, robotic process automation (RPA), machine learning (ML) and natural language processing (NLP) have emerged as influential technologies driving productivity, operational efficiency, and innovation. Each of these technologies deliver unique capabilities that complement one another to enable digital transformation across the organization.

Before we begin a discussion on gen AI, let’s review how these other technologies are applied in financial services today.

Advanced analytics: driving predictive insights in financial services

While business intelligence (BI) tools have been widely adopted in financial services to support decision-making and to monitor operations, they are used to report on past events.  Advanced analytics, on the other hand, provide the ability to make predictions on future outcomes.  These models have a wide range of applications in financial services, such as forecasting cash flows, predicting the likelihood of credit default events, executing stress testing scenarios for balance sheet management, or forecasting the performance of investment portfolios.

With the mainstream adoption of big data architectures that enable analytics on a large scale, data science has emerged as a key discipline in risk management, treasury, and capital markets. In recent years, many organizations have begun migrating to the cloud to build unified data analytics platforms that combine private and public data sources with a variety of Analytics as a Service (AaaS) components.  These technologies have made large volumes of data accessible to a broad range of users and applications across the enterprise. 

Robotic process automation: streamlining workflows and enhancing operational efficiency 

RPA excels at automating structured and repetitive workflows. By automating routine processes such as data entry, account reconciliation, and compliance reporting, RPA reduces processing time and enables operations to scale and support new business opportunities.

Intelligent Document Processing (IDP) complements RPA by leveraging AI to extract, classify, and interpret data from unstructured data sources, such as shipping documents, invoices, loan applications, stock certificates, and contracts. This technology enhances the accuracy and speed of document processing, reducing the need for manual intervention. In the operations area of financial services, where document handling is a core activity, IDP significantly improves operational efficiency.

Machine learning in financial services: enhancing risk management and fraud detection 

Machine learning models can categorize data and identify patterns or anomalies in data sets. In financial services, ML has applications in risk management, trade compliance, sales and marketing, loan operations, AML, and fraud detection. ML models can be used in decision support systems to make recommendations based on user preferences or past behavior, categorize accounts to identify customers that may be interested in products and services, or classify events in real-time to support fraud detection or compliance monitoring.

Machine learning has grown in popularity, partly due to the ability of big data technology to make large volumes of data accessible at a low cost. As more firms implement domain-specific data lakes, it becomes easier to access the historical data needed to train machine learning models. Cloud-based machine learning frameworks and AaaS offerings make these tools more accessible to a broader audience. 

Natural language processing: transforming customer service and insights 

NLP technology focuses on analysis of human language. It is capable of text and sentiment analysis, language translation, and speech recognition.  NLP is commonly used in financial services to implement chatbots and virtual assistants for customer service or internal support. NLP models analyze customer sentiment from social media feeds, and they can act as digital research assistants by summarizing financial documents, security filings, or analyst calls.

Introducing generative AI: redefining content creation and analysis 

Generative AI is closely related to NLP, in that it uses similar techniques to analyze language; however, where NLP is primarily focused on the analysis of text and speech, Generative AI is focused on the creation of new content.  Gen AI can generate text, images, audio, or video.   Further, gen AI can generate source code, spreadsheet formulas, charts, and synthetic data sets.  In addition to generating content, gen AI can edit text and format or change the layout of documents, spreadsheets, charts, or presentations. Gen AI models are capable of both human language translation and translation between coding languages.       

As with NLP, gen AI can perform semantic analysis of text. This capability is primarily used to summarize documents or meeting transcripts, but it can also be used to perform sentiment analysis to identify the tone of a document or text segment. Gen AI can even serve as a writing coach, offering suggestions to improve or rephrase a sentence or paragraph of text.

Gen AI can power intelligent search engines that use natural language prompts to search the web, enterprise content, or personal documents. It can summarize and present results in an organized, user-friendly manner that mimics a natural conversation. Additionally, gen AI can interpret prompts within the context of a conversation, allowing users to refine the output of a model through a series of commands. Some applications allow a user to control the data that a model has access to, allowing models to search and reference a user’s personal data or enterprise content the user has access to. 

Challenges and risks

While gen AI delivers an impressive range of capabilities, it also presents unique challenges.  Key concerns associated with gen AI technology include:

  • Presenting inaccurate or misleading information,
  • Providing advice that may be inappropriate or could potentially cause harm,
  • Sharing information that might not be uniformly accessible to all clients,
  • Protection of confidential or sensitive data from unauthorized access,
  • The risk of data leakage due to models exporting data outside the organization’s boundaries,
  • Model bias and ethical considerations, such as copyright infringement,
  • A lack of transparency in gen AI models, 
  • Challenges in testing due to the dynamic nature of model output,
  • High implementation costs and uncertain return on investment (ROI),
  • High energy consumption and water usage to operate models.

Mitigating risks and ensuring responsible gen AI use

Because gen AI is still a relatively new technology, there is not yet a significant body of knowledge or consensus on best practices to address some of these issues. Many financial organizations are in the early stages of piloting the technology to gain a practical understanding of its limitations and to develop effective strategies to mitigate the risks. Financial regulators are also actively reviewing the technology to develop guidance for organizations under their jurisdiction, which can pose a challenge to early adopters and multi-jurisdictional organizations.

In heavily regulated domains such as banking and capital markets, these limitations can make gen AI unsuitable for many applications. However, with careful planning and preparation, it can still be applied effectively to deliver benefits across the organization.

How generative AI transforms financial services: key applications and benefits

So how can gen AI be specifically used in financial services? 

Fraud detection and investigation

ML and predictive analytics are widely used in fraud detection. These technologies can detect patterns of behavior in real time to identify or anticipate fraudulent transactions. Gen AI can improve the quality of these models by generating synthetic data sets for model training. Mastercard recently applied gen AI to improve models that identify compromised credit cards.2 They utilized gen AI models to autocomplete obfuscated stolen credit card numbers published on the dark web, improving detection rates and nearly doubling the speed at which compromised cards were identified.

Gen AI can also assist in fraud investigations.3 Intelligent search engines built with gen AI can search, summarize, and organize unstructured data sources, such as email messages, transcripts of phone calls, video meetings, legal documents, and reports. This improves operational efficiency, allowing investigations to be resolved sooner, and alleviates the manual workload of investigators.

Regulatory compliance

Gen AI can ease the workload of compliance officers by monitoring and reporting on regulatory changes, enabling quick identification of relevant rule modifications. It can also analyze and compare documents to find similarities or differences between regulations in different jurisdictions.   

Intelligent search engines can search and summarize regulatory documents on the web, allowing users to efficiently navigate the regulatory landscape. Role-based digital assistants can facilitate queries against enterprise document repositories or a variety of unstructured data sources, such as transcripts of instant messaging conversations. This allows an auditor to efficiently locate and summarize relevant data to support an investigation.

Gen AI can also assist in generating compliance reports from templates and case management systems, ensuring timely disclosure to regulators.  For example, a gen AI application could automatically generate incident reports for disclosure of material cybersecurity incidents to security regulators.

Customer support and engagement

In the highly regulated financial services domain, the current limitations of gen AI may make it unsuitable for building autonomous chatbots that interact directly with customers, but gen AI can still be used to support human agents that interact with customers.

Microsoft Copilot for Customer Support and Cisco AI Assistant for Customer Experience are examples of role-based virtual assistants that automate customer support workflows.  These applications can transcribe and summarize phone calls, make dynamic script suggestions for speaking with customers, and provide a natural language interface for querying document repositories, such as knowledge base wikis or operational support documents. When integrated with CRM and case management systems, these tools can retrieve background information relevant to a customer’s issue and generate or make updates to a support ticket.

Market analysis and insights

The main application of gen AI for market analysis is to facilitate search and summarization of external documents. Business analysts can utilize intelligent search engines to navigate and summarize financial statements, prospectuses, regulatory filings, and analyst calls. Gen AI can analyze the sentiment of analyst calls or alert an analyst when there are news events that may be of interest based on an analyst’s preferences.

Embracing the future of financial services with generative AI and advanced technologies 

In summary, the integration of generative AI and advanced technologies into financial services presents a transformative opportunity for the industry. By leveraging gen AI, financial institutions can enhance productivity, improve customer experiences, and drive innovation. However, it is crucial to approach this integration with a structured implementation strategy to mitigate risks and ensure responsible use. As the financial services sector continues to evolve, staying ahead of technological advancements will be key to maintaining a competitive edge.

Partner with Alithya for successful gen AI integration

With a proven track record of delivering successful projects for major financial institutions, Alithya is your ideal partner to harness the power of generative AI to achieve your business goals. Our expertise in AI strategy, implementation, and risk management ensures a seamless integration of gen AI into your technology stack. Our team of experienced professionals will work closely with you to develop a tailored approach that addresses your specific needs and challenges, ensuring a successful and responsible deployment of gen AI. 

Contact us to transform your financial services operations and stay ahead in the digital landscape. 

 

1. Financial services firms embrace Generative AI 
2. Mastercard Accelerates Card Fraud Detection with Generative-AI Technology 
3. 3 ways generative AI can assist in criminal investigations

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