Over the past 5 years, many factors have had an impact on the activities of the finance department:
- Financial and accounting data are living in an era of interconnection between different business information systems,
- Rolling forecasts are becoming the norm, for greater agility,
- Managing ESG indicators requires audits, certifications and the evangelization of all departments.
Backed by our partner PWC's study of the Finance Department, our article offers, first and foremost, an overview of the impact of AI on financial activities, business lines and skills. We also offer CFO decision-makers a methodological approach to developing their department and its efficiency.
Artificial intelligence and financial activities: what synergies?
Despite the technological innovations of the last ten decades (4G, 5G, Big Data, IOT, social networks, Blockchain, ...) productivity gains are not increasing at the same rate as the period from 1973 to 2003 (0.4% last year in France vs. 2.3%). The imperative of maximizing profitability calls on the Finance Department to promote innovation through the adoption of new technologies such as AI.
With the dematerialization of invoices set to become compulsory from 2026 onwards, finance departments are at the same time working on the configuration of various business software packages, which are seeing the disappearance of activities such as data entry, the recording of accounting documents and provisioning.
Artificial Intelligence in Finance: A Mosaic of Innovative Solutions
Why talk about "artificial intelligence" in the financial sector?
In the world of finance, the term "Artificial Intelligence" (AI) encompasses a range of technologies for a variety of use cases. In fact, we should be talking about "Artificial Intelligences", as there are different forms of AI, each adapted to specific purposes.
Traditional AI for financial decision-making
What do we call Traditional AI?
Traditional Artificial Intelligence focuses on data analysis and process automation. It uses algorithms to analyze historical data sets and make predictions or automate repetitive tasks. Its main aim is to make existing processes faster and more efficient.
Here is a list of activities for which so-called traditional AI brings value:
- Risk management (Deloitte estimates a 42% reduction in false positives)
- Identification of anomalies
- Analysis of different cost categories
- Legal Compliance and Fraud Detection
Generative AI for Content Creation and Explanation of Financial Recommendations:
What do we call Generative AI?
Generative Artificial Intelligence, on the other hand, pushes the boundaries by instituting new methods of content creation. It uses advanced statistical techniques based on (L)arge (L)anguage (M)odels to generate texts, images, financial models, or even to answer complex questions. It is also very resource-intensive. This AI stands out for its ability to create and innovate, offering new perspectives in explaining the strategic decision-making recommendations provided by so-called "traditional" AI.
- Drafting of documents (contracts, correspondence with shareholders)
- Automatic Reports
- Financial Scenario Modeling
- Strategic Business Intelligence and Knowledge
What are the synergies between Traditional and Generative AI?
The combination of traditional AI and generative AI accelerates adoption for each of the finance and accounting management populations. The automatic data processing and suggestion dimension of traditional AI will benefit from the pedagogical strengths of "Gen AI" to explain the suggestions offered to users.
What approach should be adopted to integrate AI within the CFO?
Neobrain has developed a model based on various pieces of research (International Labour Organisation, Goldman Sachs, PWC...). We have supplemented it to provide the most accurate view possible of productivity gains in the finance and accounting function.
What factors need to be taken into account when estimating gains in operational efficiency?
Cultural components, training, leadership and the interconnection of information systems are decisive criteria in a company's ability to take full advantage of the efficiency gains provided by AI. We detail the factors to be taken into account below:
- Nature of tasks (complexity, human interaction, level of expertise, volume of data processed, etc.)
- The scope of the objectives pursued (what are the problems to be solved?)
- AI technology maturity (AI's ability to handle tasks based on the level of understanding, creativity or complex decision-making required)
- Integration and adaptation challenges (level of interconnection in current business systems and existing workflows, degree of employee adoption in the face of adaptation to new forms of work induced by AI tools)
Financial services haven't waited for the advent of AI to streamline their operations,
McKinsey reports a 29% reduction in operating costs over the last 10 years. After the digitization and automation of a number of financial processes, a new stage in the drive for efficiency is now underway. The challenge now is to tackle the various technological bricks simultaneously.
The Value Chain of the Finance Department's Activities
Let's look at the whole range of activities, from central financial management to stakeholder relations. In this way, we can first examine the efficiency gains made possible by AI, and then draw up a roadmap for assimilating these new tools.
Let's take a few examples to illustrate the impact of AI on some of these tasks:
IA and Financial Operations
- Contract drafting: AI optimizes the process by automating standard clauses and formats, saving time and reducing human error. However, human supervision is required for customization and complex legal nuances.
- Review of accounting records: AI's ability to manage large data sets helps to quickly identify discrepancies and anomalies in financial records, improving accuracy and regulatory compliance.
- Invoice processing : AI automates the capture and verification of invoice data, leading to faster processing and reduced manual intervention. It is particularly effective for tasks involving large volumes of data.
AI and Accounting
Generative AI can automate transaction categorization, reconciliation processes and routine queries, potentially improving productivity by around 20% to 30%. Automating repetitive tasks and reducing errors are key contributions to these gains.
AI and Risk Management
The next wave of generative AI in risk management could mean anomaly prediction and explanation, fraud detection and compliance monitoring. This advanced application could lead to productivity improvements of around 20% to 30%, enabling proactive rather than reactive risk management.
Financial Management: jobs threatened and jobs created.
Before looking at the projections for the Finance department's current professions, let's take a comforting look at one fact:
60% of today's professions did not exist 80 years ago.
Consequently, these potential job losses are part of a cycle that will also result in the creation of new professions. Faced with the turning point represented by the spread of artificial intelligence into the daily lives of employees, there are 4 job categories to keep in mind: jobs that are threatened, protected, supplemented or created.
Which DAF jobs are under threat?
The CFO jobs under threat are those jobs which involve standardized tasks, little personalization, and high exposure to strict regulatory constraints for which contexts do not vary.
According to ROME codes, the jobs most at risk are M1202: Budget Analyst, and M1203: Accountant.
What are the new jobs within DAF?
Features: high degree of customization, adaptation to changing contexts, wide range of skills.
The professions that can most benefit from additional AI are M1204: Management Controller, and M1206: Treasury.
What are the jobs protected DAF sites?
Those jobs who focus on skills interpersonal, big-picture issues, such as CFOs, remain relatively protected. Digital maturity and the ambition to create an autonomous finance sector puts Finance functional IS consultants (M1205) in a comfortable position.
The most protected professions are M1205: CFO and Finance Functional IS Consultants.
What are the new DAF professions?
AI will create new professions, particularly in the field of "prompting", i.e. the art of giving clear instructions to AI to extract its maximum potential. The interconnection of business IS with the integration of an "AI" brick is also an area of development for many roles.
The jobs created include Systems Integration and Connectivity Manager.
skills of Finance Department professionals
Professional assets differ according to jobs. Common characteristics emerge under the influence of 3 main needs:
- The rate at which we monitor and read performance is much more regular than before.
- Cost accounting is also being revised to incorporate indicators (environmental, social and institutional sustainability).
- The gradual immersion of AI calls on professionals to parameterize its field of intervention and stand back from the recommendations it is given.
What are the skills financial risks?
The skills at-risk sites include routine, automatable tasks such as accounting entry and financial statement auditing.
What are the resilient skills at DAF?
The resilient skills , on the other hand, are those that require human judgment and strategic analysis, such as project management and performance evaluation.
What emerging skills should Finance Departments be developing?
We believe that major skills are emerging to strengthen oversight, precision and efficiency in financial operations. We have identified 3 in particular:
- Advanced AI Prompting: Set up and interact with AI to detect fraud, configure automated financial alerts and optimize provisioning.
- Risk Analysis with AI: Leveraging AI for in-depth risk analysis and proactive decision-making based on predictive data.
- Optimizing Processes through AI: Implement AI to automate and refine accounting and financial procedures, increasing efficiency and accuracy.
Take auditing and accounting control, for example.
What roadmap should we take in the use of AI for financial professions?
By now, you're familiar with the impact on your business and skills: now it's time to take action. We're convinced to use the 5-step model illustrated below:
- Acculturation to AI: Organize in-house training to develop a global understanding of AI and its application in the financial businesses.
- Identifying skills and Upskilling: After acculturation, focus on developing cross-functional skills and upskilling skills to adapt to future requirements.
- Targeted Change Management: Implement a structured change plan, prioritizing businesses according to their level of impact by AI (see visual below).
- AI Charter: Establish guiding principles clarifying when and how to use AI, while highlighting situations where its use is not appropriate.
- Collaboration with Social Partners: Working closely with social partners to ensure a smooth and inclusive transition.
