The Future of Microsoft Copilot for Finance

1. Revolutionising visual dashboard through AI

Data visualisation via charts, bars and pie diagrams is easier to understand than a table of rows and columns. Over time, financial dashboards have been complex and time-consuming to produce, requiring expertise in both data manipulation and visualisation.

However, with AI-powered Microsoft Copilot for Finance, this process is expected to become considerably simpler. Now, financial professionals can instruct bots to fetch, analyse and display data from multiple sources seamlessly using natural language processing (NLP).

While using MS Office applications through Excel with embedded Copilot, a CFO could ask,

• “Show me the quarterly revenue breakdown by region” or
• “What is the Aug 2024 revenue from cosmetics for stores in Chicago” or
• “Show me the total revenue in Aug 2024 vs July 2024 from stores in Boston.”

and within seconds, a comprehensive and interactive dashboard appears. This shift not only saves time but also democratises data analysis, enabling individuals without technical expertise to harness advanced analytics.

2. Customised Copilots through Azure AI Studio (AI Hub)

The heart of this transformation lies within the AI Project (child resource of the AI Hub) – a potent tool capable of creating and using small language models (SLMs) specifically trained for various financial applications. This technology enables customised Copilots that effortlessly connect multiple application sources while utilising keyword-based search functionalities effectively.

With self-learning algorithms incorporated into the AI Project that predict common word usage and build dictionaries from large language models (LLMs) such as ChatGPT, these Copilots are becoming increasingly intuitive over time. e.g., the AI model can be trained to interpret synonyms for ‘revenue’ = income/earnings/turnover/sales/receipts/proceeds/gross income/top line/sales revenue/gross receipts. This would enable the AI model to interpret and adapt to look for ‘revenue’ from the table in the ERP system. This is based on building small language models which would be a sub-set of the LLMs and stored within the AI model.

Consider a scenario where a finance team needs to aggregate data from its Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The AI project can be trained to understand unique data structures and fields within these systems, facilitating seamless integration and comprehensive data analysis. By employing specialised language models, the Copilot can interpret and respond to queries with remarkable accuracy, providing precise and actionable insights.

3. Machine learning for enhanced precision

By analysing patterns in user interactions, machine learning enables Copilot for Finance to anticipate user needs accurately (the AI model uses the business context for predictive analysis) and offer more relevant responses.

• For example, when the initial query was on ‘what was the total sales of mobile devices in Aug 2024 from Chicago stores for company code USMF’
• The subsequent query could be ‘how many cell phones were sold in Aug 2024’
• The AI model would understand that the initial query of mobile devices includes cell phones and use that business context for providing the answer for sales in Aug 2024 from Chicago stores.

This continuous learning process ensures the models remain adaptive, capable of handling evolving financial data and queries with increased precision.

4. Personalisation through persona-enabled bots

One distinctive feature being developed for future finance Copilots is persona-enabled bots which align closely with different roles within an organisation – providing personalised experiences tailored according to individual needs.

• The finance manager’s bot focuses on high-level strategic planning tools
• The accountant’s bot prioritises detailed transactional data
• The financial analyst’s bot emphasises comprehensive analytical reports

These personas allow for model refinement through this context which allows each bot to handle tasks efficiently while ensuring relevant insights are provided quickly.

5. Robust security controls for persona-based access

Security is paramount when deploying any AI-driven solution in finance; thus, future versions of Copilot for Finance will also offer persona-based access controls, ensuring sensitive information remains protected while users execute their tasks efficiently.

All actions by these Copilots adhere to strict security protocols, ensuring authorised user access based on privileges, compliance with regulatory standards, ethical conduct, and absence of bias, qualifying them as “responsible AI.”

6. Streamlining processes

The focus is on optimising and simplifying and sequencing the completion of tasks. By doing this, organisations can enhance the quality and consistency of their processes across the board. A specific case in point could include period-end close activities, which are the steps an organisation takes to finalise its accounting records at the end of a period (e.g., month-end or year-end). This would include :

• Natural sequence: Tasks that should be completed in a logical order that reflects the actual flow of work
• Using a checklist; Ensures all necessary steps are completed
• Workflow management: Automate the process, assign tasks to the right people and ensure each step is completed before moving on to the next; also helps track progress and ensures accountability
• Appropriate levels of system access: Only authorised personnel can access certain parts of the workflow or checklist, depending on their role.

7. Seamless user experience with Natural Language Processing

The goal for future iterations of Copilot for Finance is delivering seamless user experiences, behind which lie complex processes of data integration and analysis made possible through NLP.

Users will interact naturally with their Copilot for Finance, asking questions or giving commands as if they were speaking with another person or using a tool like ChatGPT. These interactions can even take place within preferred collaboration platforms like Microsoft Teams, facilitating real-time collaboration and decision-making. These queries would be processed by MS Ecosystem Applications using contextual search and report functionality.

The future of finance with Microsoft Copilot

Copilot for finance promises to revolutionise the way financial professionals interact with data. By leveraging the AI Hub with all its components (including AI Project with components (datasets, models, indexes), Isolated data container, Project-scoped connections, Open + serverless API model deployment), Hub-scoped connections, Compute, Security setup + governance), organisations can develop customised solutions that integrate seamlessly with existing systems while delivering accurate and comprehensive data analysis.

Meanwhile, self-learning algorithms and machine learning capabilities further enhance the Copilot’s understanding of user queries, providing precise responses in an intuitive manner.

Robust security controls protect sensitive information allowing users to complete their roles effectively — all while offering a seamless user experience where complexities remain hidden behind the scenes.

As these technologies continue to be refined, Microsoft Copilot for Finance is set to become an indispensable tool driving efficiency, accuracy and strategic insight within the financial sector. The future is here – powered by AI.

Read our white paper, Enabling Enterprise AI Adoption Through Next Generation Governance.

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