Industries and businesses across the world are considering how they could benefit from artificial intelligence (AI). These industries include financial and legal services.
AI in Financial Services
The Alan Turing Institute in their report AI in financial services identified machine learning (ML), non-traditional data and automation as technological elements driving AI innovation in financial services. ML is AI that learns from data and algorithms to gradually improve its accuracy. Non-traditional data is using types of data that have not been used in the past for that use. Primarily because the data was not available or inaccessible. Automation is the completion of tasks previously completed by humans by AI, completely replacing or reducing the input of humans for that task. Within these areas, there are types of AI that can be identified as useful for the financial services industry.
Machine Learning
One approach to data analysis is ML, which is used in financial services. By processing structured data, the AI can be used to forecast, assess risk, predict costs and detect fraud. Another approach to data analysis is statistical and actuarial based methods. While there is a lot of crossover between these two approaches, ML is a step forward. ML improves upon statistical methods by creating accurate models that can process vast quantities of data, and often more quickly.
ML is also more effective at processing non-structured data. This data, which includes natural language and visual data, is harder to process due to its variability and complexity. To address this there is natural language processing (NLP) for text and computer vision for images. These types of ML are used to process this data to produce useable outputs such as generating text documents and in facial recognition.
Non-Traditional Data
AI is opening up the use of data that we previously could not access or could not use effectively. Such data can originate from many sources such as public records, satellite imagery and bank account information. Access to new sources of data allows for more rigorous and informed analysis.
Automation
Automation can make a significant impact on business activity. From routine tasks to complex tasks, automation includes AI that completes tasks autonomously and those that require oversight from a human. For example, types of tasks that can be completed entirely by AI include data handling and collection, fraudulent activity detection, and price optimisation. In other cases, the work completed by AI can be used to aid a human, but final decisions or work is completed by the human.
AI in Legal Services
While some uses of AI are similar across financial and legal services, there are some uses of AI that are of particular relevance to legal services. Such AI includes discovery and research software and document management.
Discovery and Research Software
Law requires extensive research. Reading case specific documents, legal regulations, past cases, etc. This is a time-consuming manual process to identify useful information. However, this burdensome task can be streamlined by AI. Discovery AI can be used to scan documents from non-privileged sources to identify information relevant to a case. To identify relevant case theory, regulations or other legal precedent or information, law firms can use research software. Using such AI software saves time through replacing the manual process and could identify information that a human may have missed or not had the time to find due to the time-consuming nature of research.
Document Management
It would be difficult to identify an industry heavier with documentation than law. It is important that these electronic documents are organised efficiently and can be retrieved as needed. AI can be used to organise documents, including tagging documents based on their contents so they can easily be searched for, and check-in and -out features for version control and security. Additionally, to improve document creation efficiency, automation can be used with templates to fill in form fields based on case records.
Required Caution
In both financial and legal services, caution when using AI is required. AI learns, it generates and it can be wrong. Just as doctors are responsible for their patients’ care, financial and legal companies have a responsibility to their clients. AI should be used as a tool, not as the expert. For example, AI should not be used by lawyers to write their legal briefs without at least thoroughly reviewing their contents. This was a lesson hard learned by a New York lawyer recently. Schwartz was in court for using ChatGPT, an AI chatbot that has gained significant attention, to create a court document that cited cases that don’t exist (Weiser and Schweber, 2023). Schwartz’s misunderstanding of how ChatGPT works highlights the importance of thorough training and learning when it comes to incorporating AI.
Conclusion
The capabilities of AI are expanding and with it the useful possibilities. It will be exciting to see how these AI transform industries across the spectrum, including how it will impact financial and legal services.
Interested in generative AI? In our short video we discuss how generative AI can be used in financial and legal services businesses.
Read more on the capabilities of AI in our blog AI in Business: What AI Can Do in 2023.
