Implement AI for Finance - Exploring Barriers and Benefits Part Two - BENEFITS

03 Transformational Change for the Finance Team

The implementation of AI technology in the finance department of an organization is poised to bring transformative changes.

In this blog series, we’ll explore the barriers and benefits of implementing AI solutions for the finance function in three parts, first discussing the barriers in Part One, then exploring the benefits in Part Two, and in Part Three we’ll talk about other use cases for AI in finance.

Part Two

Benefits to AI-Driven Technology for Finance

02 Overcoming Barriers to AI-Driven Technology for Finance

To overcome the obstacles associated with implementing AI solutions, organizations need to adopt well-defined strategies, focus on data quality, continuously monitor AI system performance, assess ROI, and address ethical considerations. Training and upskilling the workforce, adapting infrastructure, and developing tailored AI strategies are also key steps in successful AI adoption.

Implementing AI in the finance function of corporations, particularly in areas such as accounts receivable, accounts payable, and accounting, significantly enhance efficiency and accuracy. Here's how organizations should apply various strategies to overcome AI implementation challenges.

Addressing Data Challenges

In finance functions, the accuracy and integrity of data are paramount. AI assists in ensuring data quality by automating the validation and reconciliation of financial transactions. To tackle the lack of data and data quality issues, organizations need to foster collaborations to access relevant datasets, use techniques such as transfer learning, data augmentation, and synthetic data generation, and invest in data collection processes that align with their objectives while complying with privacy regulations​​​​. 

By implementing strategies to ensure data quality and manage large datasets, organizations improve the performance of AI applications in these areas. This involves cleansing and organizing financial data and establishing governance policies for consistent data management​​​​.

02 Enhancing Trust and Reliability

Enhancing Trust and Reliability

Building trust in AI systems involves making these systems transparent and explainable. This means providing clear explanations of how AI systems reach conclusions and emphasizing ethical AI practices. Rigorous testing, validation processes, continuous monitoring, and refinement, as well as transparency and explainability in AI algorithms, are essential for building trust​​. 

Collaborating with regulators and external experts to validate AI solutions against regulatory requirements is also important​​. Ethical considerations, especially in handling sensitive financial data, are critical. Implementing AI in a transparent and ethically responsible manner ensures data privacy and security, and is imperative to maintaining trust and compliance​​.

02 Setting Clear Goals, ROI, and Technical Preparation

Setting Clear Goals, ROI, and Technical Preparation

Designing an AI strategy with a good ROI involves determining the precise business use cases, ensuring the quality of data, and considering the costs of model accuracy, infrastructure, and ongoing maintenance​​.

Clear goals are fundamental to the success of an AI technology implementation, providing direction, focus, and a means to measure progress and success. They enable efficient allocation of resources and help manage risks associated with AI projects. 

Clear objectives also facilitate stakeholder buy-in, ensuring that all parties are aligned and supportive. They guide the selection of appropriate technologies and methodologies, ensuring that the AI solution meets the organization's specific needs. Additionally, well-defined goals are important for ensuring regulatory compliance, especially in a data-sensitive sector such as finance.

Investing in a robust infrastructure to handle AI data ensures data security and privacy, and allows for scalability from the outset​​. Implementing AI solutions with clear goals and ROI strategies greatly enhances the financial planning and analysis functions​​. 

02 Addressing Data Challenges

Mitigating Algorithm Bias 

AI in finance is heavily used for risk assessment and management, including credit scoring, market risk, and operational risk analysis. The models used must be transparent, explainable, and regularly audited for accuracy and bias.

Organizations need to incorporate policies to promote fairness and inclusivity, regular audits, and bias mitigation techniques​​. To address bias in AI algorithms, companies need to select and preprocess training data to minimize biased patterns, develop bias detection and mitigation techniques, and conduct regular audits to ensure ongoing fairness​​. 

Regulatory Compliance and Updating Legacy Systems

Another critical area where AI will impact is in regulatory compliance. AI systems are capable of ensuring adherence to financial regulations and standards, which is crucial in today's rapidly evolving regulatory landscape.

Navigating regulatory landscapes requires a balance between innovation and compliance. Addressing regulatory compliance challenges with AI leads to more robust financial control mechanisms​ and better protection for the organization. 

Integrating AI into legacy systems requires a substantial overhaul of existing infrastructure and data pipelines. Organizations should conduct compatibility assessments and develop a clear integration strategy​​​​.

With the increasing use of AI in handling sensitive financial data, there is a heightened focus on cybersecurity and data protection. This includes ensuring that AI systems are secure from external threats and that customer data is handled responsibly. 

02 Workforce Impact

Tackling Talent Shortage

To overcome the shortage of skilled AI professionals, organizations should focus on training and upskilling existing employees to adapt to new roles that require human skills such as creativity and critical thinking​​. Finance needs to foster an internal culture for AI development, and keep an eye out for promising talent to bring onboard​​​​. 

A small team of project-based data scientists, machine learning engineers, and data architects could also be a means to quickly spin up AI pilots and perform cadenced maintenance without incurring massive overhead. Once success is proven and an organization starts seeing value, finance teams could scale up and eventually justify permanent hires to help manage the AI data models. 

02 Tackling Talent Shortage

Workforce Impact

Educating customers about the benefits of AI and managing their expectations are vital to a success finance operation. Proactively inform employees about the AI implementation, its purposes, and the expected changes. Transparency helps in alleviating fears and misconceptions. Train leaders and managers on how to effectively manage teams in an AI-enhanced environment, focusing on empathy, change management, and effective communication.

Similarly, addressing workforce impact involves upskilling and retraining employees in AI and data analytics, which in turn empowers them to work alongside AI systems, enhancing their roles rather than replacing them. Invest in training programs to help employees adapt to new roles to include training in AI application management or other complementary skills. Provide opportunities for career advancement within the new structure, showing employees a path to growth despite the changes.

Regularly monitor the impact of AI on the workforce and be open to feedback. This will help in making timely adjustments to an organizations AI strategy and execution.

02 Global AI Use and Coverage

Global AI Use and Coverage

Financial institutions using AI must comply with existing regulatory frameworks such as GDPR in Europe, the Bank Secrecy Act in the USA, and other regional regulations. These regulations often focus on data privacy, ethical use of AI, and preventing money laundering and financial crimes. 
To help organizations in their quest for advanced technology, AI governance should be part of the organization’s governance landscape and intertwine with IT governance, data governance, and general governance to safely operate an AI system. 
Standards for AI use are continuously evolving as technology advances and as the global financial community gains more experience with AI applications. Organizations should seek out continuous updates and stay abreast of updates to standards and governance through education, AI-related association memberships, and thought leadership articles published in industry journals and analyst reports.

Prioritize AI for Finance

Gartner relayed that finance leaders cited “other priorities” as a barrier to implementing AI solutions. Gartner believes this speaks to an important aspect of finance leaders’ beliefs about AI —that it is a discrete project that would need to be added separately to their function’s transformation roadmap. What this perspective underappreciates, however, is that AI is a critical enabler of finance leaders’ “other priorities,” such as more dynamic financial planning or close and consolidation efficiency.
To direct the focus, budget, and approval for AI-tech related finance projects, finance leaders and managers should be coached to view AI as a facilitator of finance priorities by deepening their understanding of the AI’s capabilities and identifying areas where AI improves process and workflow efficiency. 
Finance leaders should explore the AI-enabled capabilities of business automation platforms and intelligent applications by placing AI at the forefront of all vendor discussions pertaining to current and new technologies.

Read on:

Part One - Barriers to AI Solution Implementation for Finance

Part Three - Additional Finance Use Cases

Source Files:
The Current State of AI Use Within Finance: 2023 Insights, 4 October 2023 - ID G00799199 - Gartner, Rajat Pandey, Marco Steecker
Quick Answer: What Are the Quick-Win Use Cases When Selling GenAI to Finance Leaders? 5 January 2024 - ID G00803754 - Gartner, Garrett Astler