Daniele Adami, Enterprise Account Manager at Cloudera, explains how to accelerate cost reduction in financial services by adoptingArtificial intelligence.
In the ever-changing landscape of the financial services industry, change is a constant and transformation a requirement to keep up with new regulations, mitigate risk and manage the technological developments that support it.
Finding the right balance between innovation and savings, looking for opportunities to reduce expenses and finance new application development projects is a widespread objective. Today, the landscape is changing rapidly, and the emergence of AI and machine learning (ML) has ushered in a new era of possibilities. These cutting-edge technologies offer low-cost alternatives to enhance the efficiency of financial activities, while improving the quality of the services offered.
According to Cloudera, the use cases for these technologies continue to expand and improve, shaping the industry in ways we can only dream of, with insights aided by the power of large language models (LLMs), confirming the strength of the synergy between human skills and AI-driven insights.
AI in general and ML, Generative AI LLM in particular, they have the potential to significantly reduce the costs of financial services by automating tasks, improving productivity and reducing the need for manual labor. Furthermore, by investing in these technologies, companies increase competitive advantage and improve customer experience.
Cloudera – Enterprise Knowledge Base (EKB) e Chatbot
Per Cloudera, i chatbot, or virtual assistants powered by generative artificial intelligence, can be used to create tools that evaluate user requests and provide personalized responses, reducing the need and costs associated with human representatives. They can provide instant and accurate answers to the most common questions, such as account balance, transaction history, loan information and general banking procedures.
Automated search and reporting
Automate the process of collecting, analyzing and reporting financial data and market trends, reducing the time and cost of manual research to accelerate decision making.
Cloudera – Portfolio Optimization Analyze an investment portfolio and identify opportunities to optimize returns and manage 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 FAQ and help sections, keeping customer-facing content up to date.
Automatically create personalized messages, statements and transaction summaries and notify customers of upcoming events or offers. Group customers based on transaction history, demographics, behavior patterns and other relevant data to improve marketing and personalization results. Detect patterns and indicators of potential fraudulent activity using transaction data, customer profiles and other relevant information. Legal and compliance
Summarize the regulatory requirements: AI can summarize regulatory requirements by analyzing large volumes of text and extracting key information, making it easier for financial services companies to understand and comply with complex regulations.
Automate compliance monitoring: Analyzing and interpreting regulatory texts is time-consuming, but AI can automate these tasks to help financial services companies understand and comply with complex regulations, saving time and costs associated with manual monitoring.
Improve management of regulatory changes: Financial services companies must stay abreast of regulatory changes by monitoring and analyzing updates. AI can enable them to quickly adapt processes and reduce the risk of non-compliance.
Improve regulatory reporting: AI can automate the regulatory reporting process, ensuring accuracy and consistency while reducing the time and effort required for manual reporting.
Accelerated legal research: Generative AI tools can quickly search and analyze case law, legislation and secondary sources, allowing legal professionals to easily access relevant information. Cloudera – Language translation
Multilingual customer service: with generative AI, support representatives can effectively communicate with customers who may not speak the bank’s primary language.
Document translation: When working with multinational groups, generative AI can translate contracts, agreements, policies and other legal and 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 developers’ time and effort. By analyzing existing code and models, generative AI algorithms can generate new code optimized for specific use cases.
Test: Generative AI can be used to automatically generate test cases, reducing the time and effort required for developers to test code. By analyzing this and identifying potential edge cases, generative AI algorithms can generate test cases that cover a wide range of scenarios.
Debug: Identifying and fixing bugs in your code are essential to application security. By analyzing code and identifying potential problems, generative AI algorithms can suggest fixes.
Optimization: By analyzing 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 and training machine learning models, helping developers improve the accuracy of their models and make more informed decisions.
Automatic generation of documentation: Generating documentation takes time and effort. AI can automatically generate documentation as developers write code, reducing the need for manual documentation and improving efficiency.
Code comments: Generative AI can be used to generate code comments that describe the function and purpose of each element, helping developers identify inconsistencies or potential sources of error.
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 manage and maintain:
Unit test generation: automatically generate unit tests for legacy code, helping developers identify and fix potential problems and improve code quality.
Code refactoring: assistance in refactoring legacy code, suggesting improvements and identifying potential sources of bugs or inefficiencies.
Legacy code translation into modern languages: Translate legacy code written in obsolete languages, such as COBOL, into more modern languages, making it easier to maintain and integrate with newer systems.