Your Money: Hurdles to applying AI in financial planning

By David Wentz Breathtaking advances in artificial intelligence have dominated the news this year, as a broad swath of industries have begun considering how AI might impact their operations in

David Wentz of Tax Favored Benefits, Inc.

By David Wentz

Breathtaking advances in artificial intelligence have dominated the news this year, as a broad swath of industries have begun considering how AI might impact their operations in the coming years.

And financial planning is no exception. While some people have started to think through applying AI to the task of managing investments, the technology faces a number of issues in the sector.

These include:

  • Data quality and availability: AI systems require large volumes of accurate and relevant data to generate reliable insights and predictions. However, financial data can be complex, fragmented, and inconsistent, making it challenging to obtain high-quality data for analysis. Data privacy and security concerns also add to the challenge of accessing financial data.
  • Interpretability and explainability: AI models often operate as black boxes, making it difficult to understand and explain the reasoning behind their predictions or recommendations. In the context of financial planning, interpretability and explainability are crucial for building trust, complying with regulations, and understanding the potential risks associated with AI-driven decisions.
  • Regulatory compliance: Financial planning involves adhering to various regulations and compliance requirements, such as anti-money laundering (AML), know your customer (KYC), and suitability rules. AI systems must navigate these regulations accurately, ensuring that the generated recommendations or decisions align with legal and ethical standards.
  • Uncertainty and market dynamics: Financial markets are complex, dynamic, and influenced by various unpredictable factors. AI models often struggle with incorporating and adapting to uncertainties and market fluctuations, which can impact the accuracy and reliability of financial planning outcomes. Robust risk assessment and forecasting capabilities are essential but challenging to achieve.
  • Bias and fairness: AI systems can inadvertently inherit biases from the data they are trained on, leading to biased outcomes in financial planning. Biases can affect decision-making processes, pricing, lending, and investment recommendations, potentially leading to unfair or discriminatory outcomes. Addressing and mitigating bias is a significant challenge for AI in the financial domain.
  • Human-AI collaboration: Financial planning involves a delicate balance between human expertise and AI-driven automation. Integrating AI systems into existing financial workflows, ensuring effective collaboration
    between humans and AI, and facilitating the transfer of decision-making responsibilities pose challenges related to user experience, training, and change management.
  • Model robustness and adaptability: AI models need to be robust and adaptable to changing market conditions, financial regulations, and customer preferences. Continuously monitoring and updating models to maintain their performance and relevance is a challenge due to the need for extensive computational resources, time, and expertise.

Addressing these challenges requires a multidisciplinary approach, involving domain expertise, regulatory understanding, data quality measures, ethical considerations, and ongoing research and development in the field of AI.

This Sponsored Column is by Tax Favored Benefits Inc., an Overland Park-based financial investment firm that works with clients to create custom wealth management strategies driving by your needs. Our experienced professionals provide a full suite of wealth, investment and retirement planning services to clients across the country.

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