In the ever-evolving landscape of the financial services Industry, change is a constant and transformation is a requirement—to stay at pace with new regulations, risk mitigation, and the technological developments that support transformation. And just as financial services experiences its cycles, this time of year I find myself returning to the topic of cost reduction.
Financial services institutions are constantly challenged to strike the delicate balance between innovation and cost-saving as they seek out opportunities to decrease expenses while funding new application development projects. Historically, firms have relied on high-cost, third-party solutions to help identify savings opportunities, however, the landscape is rapidly changing, and the emergence of AI and machine learning (ML) has ushered in a new era of possibilities. These cutting-edge technologies provide lower-cost alternatives for discovering efficiencies within financial operations, all while enhancing the quality of services offered. In this dynamic environment, harnessing the power of AI and ML is proving to be a game-changer for firms seeking to thrive in an increasingly competitive market.
The use cases for these technologies continue to expand and improve, shaping the industry in ways we could only dream of. In fact, some of the insights presented in this blog have been assisted by the power of large language models (LLMs), highlighting the synergy between human expertise and AI-driven insights.
In this year’s post, I’ve collected cost-reduction strategies—augmented by the capabilities of generative AI—and innovative approaches that promise to accelerate cost-reduction and reshape the financial services landscape in the year ahead.
New technology means new opportunities
AI generally and ML, generative AI, and LLMs specifically, have the potential to significantly reduce costs for financial services at large by automating tasks, improving productivity, and reducing the need for manual labor. And by investing in these technologies, companies gain the added benefits of increasing competitive advantage and improving customer experience.
Although this list is not exhaustive, I’ve provided an at-a-glance aggregate of use cases for quick wins, quicker wins, and longer-term strategies.
- Enterprise Knowledge Base (EKB) and Chatbots
- Chatbots, or virtual assistants powered by generative AI, can be used to create customer-facing and employee-facing tools that assess user requests and provide personalized responses, reducing the need for human representatives and related costs. They can provide instant and accurate responses to common customer queries such as account balance, transaction history, loan information, and general banking procedures.
- An EKB can be used to assist employees in accessing accurate and up-to-date product, regulatory compliance, and internal IT support information.
- Automated Research and Reporting
- Automate the process of gathering, analyzing, and reporting financial data and market trends, reducing the time and cost of manual research for faster decision-making.
- Portfolio Optimization
- Analyze a portfolio of investments and identify opportunities to optimize returns while managing risk. This can help investors reduce the time and cost of manual portfolio management and potentially improve investment performance.
- Content Generation, Text Classification, and Clustering
- Automate website content for FAQs and help sections, keeping customer-facing content up to date.
- Automatically create personalized messages, account statements, and transaction summaries, and notify customers of upcoming events or offers.
- Group customers based on their transaction history, demographics, behavior patterns, and other relevant data to improve marketing and personalization outcomes.
- Detect patterns and indicators of potential fraudulent activities using transaction data, customer profiles, and other relevant information.
- Legal and Compliance
- Summarizing regulatory requirements: AI can summarize regulatory requirements by analyzing large volumes of regulatory texts and extracting key information, making it easier for financial services firms to understand and comply with complex regulations.
- Automating compliance monitoring: Analyzing and interpreting regulatory texts is time consuming, but AI can automate these tasks to help financial services firms understand and comply with complex regulations, saving time and costs associated with manual compliance monitoring.
- Enhancing regulatory change management: Financial services firms must stay up to date with regulatory changes by monitoring and analyzing regulatory updates. AI can enable them to quickly adapt their compliance processes and reduce the risk of non-compliance.
- Improving regulatory reporting: AI can automate the process of generating regulatory reports, ensuring accuracy and consistency while reducing the time and effort required for manual reporting.
- Expedited legal research: Generative AI tools can quickly search and analyze relevant case law, legislation, and secondary sources, enabling legal professionals to access pertinent information with ease.
- Language Translation
- Multilingual customer service: With generative AI, customer support representatives can communicate effectively with customers who may not speak the bank’s primary language.
- Document translation: When collaborating with multinational groups, generative AI can translate contracts, agreements, policies, and other legal/ business documents ensuring accurate written communication.
- Code Development and Testing Assistance
- Code generation: One of the benefits of generative AI is that it can be used to generate code automatically, reducing the time and effort required by developers. By analyzing existing code and patterns, generative AI algorithms can generate new code that is optimized for specific use cases.
- Testing: Generative AI can be used to generate test cases automatically, reducing the time and effort required for developers to test their code. By analyzing the code and identifying potential edge cases, generative AI algorithms can generate test cases that cover a wide range of scenarios.
- Debugging: Identifying and fixing bugs in code is essential for application security. By analyzing the code and identifying potential issues, generative AI algorithms can suggest fixes that can be implemented by developers.
- Optimization: By analyzing the code and identifying potential optimizations, generative AI can update code automatically, improving performance and reducing resource usage.
- Synthetic data generation: Generative AI can generate synthetic data for testing purposes and for training machine learning models, helping developers improve the accuracy of their models and make more informed decisions.
- Automated documentation generation: Generating documentation is time consuming and tedious. AI can automatically generate documentation as developers write code, reducing the need for manual documentation while improving efficiency.
- Code comments: Generative AI can be used to generate code comments that describe the function and purpose of each element of the code, helping developers identify inconsistencies or potential sources of errors.
- Understanding legacy code: Generative AI can be used to analyze and understand the structure and functionality of legacy code, making it easier for developers to work with and maintain, including:
- Generating unit tests: Automatically generate unit tests for legacy code, helping developers identify and fix potential issues and improve code quality.
- Code refactoring: Assist in refactoring legacy code by suggesting improvements and identifying potential sources of bugs or inefficiencies.
- Translating legacy code to modern languages: Translate legacy code written in outdated languages, such as COBOL, to more modern languages, making it easier to maintain and integrate with newer systems.
Utilizing AI and its related technologies for cost reduction may seem like a huge lift, but the opportunities in financial services are many, and overcoming the perceived challenges is getting easier every day.
Cloudera offers a variety of solutions designed to transform AI initiatives into tangible, cost-reducing outcomes. Facilitate experimentation and accelerate the development of AI applications within Cloudera Machine Learning using Applied ML Prototypes (AMPs). Deploy internal LLMs and vector databases with LLMs trained on private data sets in a secure environment to experiment without risk, all while safeguarding sensitive data on premises, ensuring compliance and reducing the costs associated with prolonged cloud-based workloads.
We’re here to help you execute on your cost-reduction strategies. If you are interested in learning more about Cloudera’s solutions for enterprise AI or discussing any of the above use cases, please reach out to me.