This is the third in a series of blogs on AI in the finance function by Ben Wright, AI specialist at Aptitude. His first blog looked at 10 AI use cases for enterprise finance data and the second discussed, why Fynapse is made for AI.
Introduction
In recent years, the financial landscape has undergone a significant transformation with the integration of artificial intelligence (AI) technologies. AI improves efficiency, accuracy, and automation in finance, but finance teams struggle to adopt and implement these technologies. This article discusses the challenges finance teams encounter when using AI for automation and decision-making. It also explores the strategies they are employing to overcome these difficulties.
Data quality and integration
One of the primary challenges facing finance teams is the quality and integration of data. AI needs reliable data for accurate financial insights.
Finance teams often struggle with data silos, where information is scattered across various systems and departments, making it difficult to integrate and analyze effectively. For example, certain solutions may not integrate third-party data, resulting in incomplete analysis. Or older database systems may be limited to updating at set intervals.
Inaccurate or incomplete data can lead to flawed AI predictions, potentially jeopardizing financial decision-making. Unlike older database systems which typically update at set intervals and rely on data tables to be linked correctly and interrogated, Aptitude’s finance data management and accounting platform, Fynapse, offers instantaneous, real-time data. This is critical for AI analysis as it provides the most current information to make accurate decisions.
Successful finance teams are investing in advanced data management platforms that facilitate seamless connectivity between disparate systems and departments. These platforms break down data silos and provide a centralized environment for data storage and analysis. Cloud solutions allow finance teams to easily combine data from different sources in real time, providing scalability and flexibility.
Skill gap and talent acquisition
Implementing AI technologies requires a workforce with specialized skills in data science, machine learning and AI development. Finance teams lack skills for new technologies, as traditional finance roles didn’t need expertise in these areas. Finance teams face a significant challenge in hiring and training staff with the necessary AI skills. This challenge is particularly daunting because of the rapid pace of technological advancements.
Successful finance teams are taking a multifaceted approach to address skill gaps. For example, Finance teams are exploring AI platforms with user-friendly interfaces that require less coding and technical expertise. These platforms assist finance professionals in utilizing AI tools without requiring programming skills, making it more accessible for them to contribute to AI initiatives. Young finance professionals want self-service data, reporting, and analytics. Being open to AI will attract professionals with the right skills.
Ethical and regulatory concerns
AI in finance brings about ethical and regulatory challenges that finance teams must navigate carefully. With the increasing use of AI in decision-making processes, questions about bias, fairness, and accountability arise. Finance teams need to ensure that AI algorithms are transparent, explainable, and comply with existing regulations.
AI technology changes quickly, making it hard for regulators to keep up. Finance teams need to stay watchful to follow ethical and legal rules.
Successful finance teams are creating strong controls for AI, focusing on transparent data and following regulations. As AI technologies evolve, finance teams will need to collaborate closely with regulatory bodies to stay updated on complexities.
Model interpretability and explainability
Finance teams often rely on complex AI models for tasks such as risk assessment, fraud detection, and investment recommendations. However, the opacity of these models can sometimes pose a challenge in terms of interpretation and explainability. Finance professionals need to understand how AI makes conclusions or predictions for important decisions.
The lack of model interpretability can hinder trust in AI systems and limit their adoption in critical financial processes. Data, calculations, and reporting must be transparent and auditable to withstand scrutiny from regulators, shareholders, and stakeholders.
Successful finance teams must fully comprehend models and algorithms to build trust in AI-driven decisions. The ability to set up clear parameters and help AI tools ‘learn’ business and risk profiles can help to guard against AI black boxes. Keeping AI models ‘in-house’ versus relying on third-party models can also help ensure control and trust.
Data security and transparency
Besides data quality and integration, ensuring data security and transparency is crucial. AI in finance involves handling sensitive information, necessitating stringent security measures and compliance with regulations like GDPR and HIPAA.
Successful finance teams need to use access controls and authentication to limit access to sensitive data. They should also employ encryption to safeguard the data during transfer and storage. This ensures that even if unauthorized access occurs, the data remains unreadable. Looking for vendors with SOC accreditation can also provide assurances about data security and risk management.
Conclusion
AI is important for autonomous finance and has the potential to transform financial processes. However, finance teams face significant obstacles when implementing it.
Overcoming these challenges requires a holistic approach that addresses issues related to data quality, talent acquisition, ethical considerations, model interpretability, and cybersecurity. Finance professionals must balance innovation and risk mitigation when harnessing the benefits of AI.
Interested in learning more? Reach out for a demo of Fynapse and we’ll show you the AI use cases we’re exploring.
