AI in Finance: Driving Efficiency and Delivering Core Customer Results

The finance industry is transforming, with generative AI (Gen AI) emerging as a game changer. Beyond streamlining back-office operations, Gen AI is revolutionizing the way financial services deliver the core outcomes that customers expect.

In this article, we explore how Gen AI is being applied in finance, with a dual focus on efficiency improvements and enhanced service delivery, and provide concrete examples from the Big 4 firms.

The Dual Focus of Gen AI in Finance

1. Efficiency Improvement

• Automation of Routine Tasks: Gen AI automates repetitive processes such as data entry, document verification, and transaction matching. This reduces manual labor and minimizes human error.

• Accelerated Decision-Making: By processing large datasets at lightning speed, AI-driven systems provide real-time insights that enable faster, data-driven decisions.

2. Delivering Core Expected Results

• Enhanced Accuracy and Transparency: Gen AI-powered systems improve the quality of financial reports and ensure compliance by accurately flagging discrepancies and anomalies.

• Improved Customer Experience: With natural language processing (NLP) capabilities, AI tools can generate clear, client-friendly reports and explanations, bolstering trust and engagement.

According to PWC’s 28 Annual Global CEO Survey

Expectations for GenAI remain high. One-third of CEOs say GenAI has increased revenue and profitability over the past year, and half expect their investments in the technology to increase profits in the year ahead. Yet trust remains a hurdle to adoption.

Overcoming the Trust Barrier in AI Implementation

Despite its transformative potential, lack of trust continues to be a significant barrier to the widespread adoption of AI in finance. Concerns around data privacy and accuracy, algorithmic transparency, and potential biases are common among stakeholders.

To overcome these challenges, financial institutions must prioritize transparency by implementing explainable AI (XAI) systems that provide transparent, understandable decision-making processes. Establishing robust governance frameworks that ensure accountability and ethical AI usage can also enhance stakeholder confidence.

Additionally, continuous monitoring and validation of AI models, combined with comprehensive education and training programs for staff, are essential to build trust and encourage responsible AI adoption. All global consulting firms have developed and actively implemented their own AI models, which they use in various fields. We provide some examples.

How the Big 4 Leverage Generative AI

The Big 4 accounting firms are at the forefront of integrating Gen AI into financial services. Some have developed a proprietary AI-enabled platform that integrates advanced generative models—built on state‐of‐the‐art natural language processing (NLP) and machine learning frameworks—with their existing data analytics systems.

This integration allows the platform to ingest vast amounts of structured and unstructured data (from financial statements, contracts, and transactional records), automatically extract key information, and flag anomalies that may require further human review.

In essence, the system automates many of the routine data processing and risk assessment tasks traditionally performed manually by auditors.

Measurable Outcomes

According to their communications, the integration of generative AI into audit workflows has led to notable improvements:

• Processing Time Reduction: Audit tasks that previously required extensive manual review are now completed up to 30–40% faster. This acceleration is mainly due to the AI’s ability to quickly sift through and analyze large datasets, dramatically reducing turnaround times.

• Error Minimization and Cost Savings: By automating repetitive and data-intensive processes, the platform reduces the risk of human error and allows audit teams to focus on higher-risk areas. The overall effect is a leaner process that not only cuts operational costs but also increases the reliability of the audit outcomes.

Concrete Functions of the Gen AI Model

The generative AI model in the described platform is designed to:

• Automate Data Extraction: It reads and interprets financial documents, extracting key figures and textual insights without manual intervention.

• Identify Anomalies and Risk Areas: By analyzing data patterns, the model flags unusual transactions or discrepancies that warrant further examination.

• Generate Summaries and Reports: The AI produces natural language summaries that provide auditors with actionable insights, streamlining the decision-making process.

• Support Compliance and Documentation: It assists in ensuring that all audit documentation is accurate and complete, supporting compliance with regulatory standards.

These capabilities collectively enable Big 4 firms to deliver more timely, accurate, and cost-effective audit and advisory services.

Another example of implementing the Gen AI model is in tax advisory services. This solution is designed to interpret complex tax regulations and vast amounts of financial data, enabling more efficient and accurate preparation of tax filings and compliance reports.

How the AI Model Works

• Data Extraction and Analysis: The AI system uses advanced natural language processing to scan and interpret legal texts, regulatory documents, and client financial records. It automatically extracts relevant provisions and key figures from these documents, ensuring no critical details are overlooked.

• Automated Report Generation: Once the necessary data is extracted, the generative AI model drafts preliminary tax filings and compliance reports. These drafts include tax liabilities and risk areas summaries, allowing tax professionals to review and finalize the reports more efficiently.

• Risk Identification and Continuous Learning: The model flags potential compliance risks and anomalies in client data, ensuring that issues are identified early. Additionally, it is designed to update continuously with the latest changes in tax laws and regulations, keeping the advisory services current.

Measurable Outcomes

• Increased Efficiency: Big 4 firm reports that incorporating generative AI into its tax processes has reduced turnaround time by approximately 25–30%. This significant improvement allows their teams to handle more complex cases without compromising quality.

• Cost Reduction and Error Minimization: The AI-driven process minimizes human error by automating repetitive tasks. It reduces the labor-intensive aspects of tax filing, leading to cost savings and higher accuracy in compliance efforts.

AI Models, Costs, Skills, and Technological Considerations

For finance professionals looking to either build their AI models or invest in AI development, understanding the landscape is crucial:

1. Off-the-Shelf AI Models

Examples: OpenAI’s GPT-3.5 and GPT-4 are available via APIs from platforms like Microsoft Azure OpenAI Service.

Cost: Pay-per-use pricing models make these accessible for pilot projects and medium-scale applications.

Skills Required: Basic integration skills, prompt engineering, and some understanding of data processing.

Pros: Quick deployment, continuous updates, and a strong community support base.

Cons: It may require customization to meet specific industry needs.

2. Custom Fine-Tuned Models

Approach: Organizations can fine-tune existing models on proprietary data to achieve higher domain-specific accuracy.

Cost: Higher upfront investment due to customization and training requirements.

Skills Required: In-depth knowledge of machine learning, data science expertise, and robust IT infrastructure.

Pros: Tailored solutions that align with specific business processes.

Cons: Requires ongoing maintenance and can be resource-intensive.

3. Proprietary AI Platforms

Overview: Many Big 4 have developed proprietary platforms integrating Gen AI with their broader service ecosystems. These platforms are designed for scalability and deep integration with financial systems.

Cost: Typically high, suited for large-scale enterprise deployments.

Skills Required: Cross-functional teams including AI specialists, software developers, and finance domain experts.

Pros: Fully integrated solutions that offer end-to-end process automation and advanced analytics.

Cons: Long development cycles and significant capital investment.

Empowering Finance Professionals

Whether you’re part of a large organization or a forward-thinking finance team, the choice between developing in-house models or partnering with external providers depends on several factors:

• Scale and Complexity of Operations: Smaller organizations may benefit from off-the-shelf solutions, while larger enterprises might find value in custom or proprietary platforms.

• Budget Constraints: Evaluate the cost-to-benefit ratio, considering immediate efficiency gains and long-term strategic advantages.

• Availability of In-House Expertise: Custom models offer competitive differentiation if you have a robust team of data scientists and ML engineers. Linking cloud-based AI services is the most effective route.

Conclusion

The application of generative AI in finance is no longer a futuristic concept—it’s a present-day reality that delivers tangible benefits in efficiency and core service outcomes.

With concrete examples from the Big 4, finance professionals now have a blueprint for harnessing these technologies. By carefully evaluating the available models in terms of cost, required skills, and technological readiness, organizations can choose the path that best aligns with their strategic goals. The future of finance is here, and AI powers it.

More about Regulating AI in the EU can be found in this post.