Gartner defines an autonomous finance function as one capable of delivering augmented real-time and predictive insights, effortless compliance, and greater flexibility in financial strategy. This means processes and activities are partly governed and majority operated by self-learning software agents that optimize front-, middle- and back-office operations.
AI is a key element of the roadmap to autonomous finance. AI, when used right, can provide new insights, automate decisions, and lessen manual work for finance teams.
Fynapse is the most forward-thinking, AI-ready Finance Hub on the market. Through our open architecture and APIs, Fynapse can access and bring together real-time, finance-controlled data sets to create a trusted, AI-ready data foundation.
We’ve compiled 10 of the AI use cases we’re most excited about.
AI use cases
By using machine learning, a user can automatically detect unusual transactions by analyzing past data and familiar patterns. This method improves the detection of fraud and the management of risk. It does so by ensuring that financial transactions align with expected patterns. Additionally, it sends alerts for any unusual behavior. This also ensures finance professionals can use their time effectively.
Analytics and reporting
Finance users can ask nuanced, conversational questions about reports or charts, making data analysis more interactive, understandable and adaptable. This transformation takes financial reporting from being a static process to a perceptive and flexible practice.
Data analysis and data model generation
Finance teams can use unstructured data to find and connect data points, creating a data model for the product. Then, they can use natural language processing (NLP) to extract meaningful insights from unstructured data. This automated process enhances data analysis, providing a comprehensive understanding of client relationships and preferences. Additionally, the ability to configure these data models within the product streamlines decision-making processes and enhances overall client management.
Addressing compliance change
Regulatory change in Financial Services has risen by 500% since the 2008 crisis, increasing compliance costs. NLP can analyze and classify documents, including customer contracts, and extract useful information like client details, products and processes affected by regulatory change. This can reduce the cost and risk of non-compliance and free up resources for other tasks.
Product documentation & training
AI allows teams to source product instructions, definitions and general information, tailored to a user’s skill level and language. AI-driven product documentation and training can improve user skills and reduce onboarding time. This can help finance teams utilize a solution’s full capabilities quickly and efficiently.
Predictive risk management
AI can be used to predict potential financial risks by analyzing patterns and trends in historical and real time data. For example, machine learning algorithms can predict market fluctuations, helping financial institutions make informed investment decisions. Natural language processing can enable the analysis of news articles and social media to gauge market sentiment and potential risks. AI models can also assess credit risk by analyzing customer behavior and financial history and automate the approval process base on the parameters and rules set by an organization.
Generative financial forecasting
AI could enable finance departments to take a set of forecasting principles and equations and allow users to generate forecasts based on human language queries. Introducing A/B testing into the forecasting process would allow the finance team to compare different forecasting models or scenarios side-by-side, evaluating which assumptions or variables yield the most accurate predictions. For example, a user could state, “what happens if…” and get useful results based on their historical data and some set equations. The generative financial forecasting feature not only enhances the accessibility of forecasting tools and brings in public domain data but also democratizes financial insights, enabling a broader spectrum of team members to actively contribute to and understand the forecasting process.
Finance teams receive AI-based reports and summaries of recent system activity delivered to users on a screen or by email. Morning reports provide important information and highlight any problems with the system at the start of the day including a comprehensive overview of key metrics and potential areas of concern. This allows finance teams to stay updated and take necessary actions.
Product troubleshooting and logic analysis
If users have questions or issues and want to understand the flow of their data, AI can help. AI-driven troubleshooting speeds up problem-solving. It not only expedites issue resolution but also empowers finance teams to gain deeper insights into the intricacies of their data flows, fostering a more proactive and informed approach to data management.
Accounting rules consolidation
Analyze your set of accounting rules to understand which are redundant and how to streamline them. This can reduce the time to close and the number of rules and logic you are managing. AI-powered accounting rules consolidation improves efficiency. It also enables finance teams to adjust rule structures based on changing business needs. This creates a more flexible financial ecosystem.
See Fynapse in action
Interested in learning more? Reach out for a demo of Fynapse and we’ll show you the AI use cases we’re exploring.