Demystifying AI in Finance: Knowledge Gaps and Mitigation Strategies

AI’s contribution to finance opens exciting paths for improving operations, enhancing customer satisfaction, and boosting growth.

Integrating AI into financial services is expected to save over $1 trillion in operating costs by 2025, leading to more streamlined and secure financial processes.

Check the benefits of AI implementation in finance (source):

• Fraud Detection and Anti-Money Laundering: AI has become a crucial component in ensuring the safety of financial transactions, with over 87% of industry leaders adopting it for these critical operations.
• AI-Driven Customized Financial Advice: Expected to grow at a CAGR of 12.6% between 2021 and 2026, AI-powered advice is set to revolutionize financial guidance.
• Operational Efficiency: AI technologies can automate processes within the finance sector, paving the way for a transition to more sophisticated, AI-driven operations.
• Customer Service: By 2025, AI will manage 26 trillion minutes of customer service interactions for financial services, significantly enhancing the customer experience.
• Investment and Growth: Also, by 2025, AI applications are expected to represent 25% of global investment in non-human, underwritten loans. Financial firms that invest in AI are likely to see an average growth rate of 35%, outpacing their competitors.

Financial institutions face various issues when implementing AI technologies, including generative AI (GenAI), which is a subset of AI focused on generating new content, insights, or data based on its training.

These obstacles span technical, talent, strategic, and regulatory domains, reflecting AI’s complexity and evolving nature in the banking and financial services sector.

• Shortage of In-House AI Capabilities: Financial institutions often lack in-house AI capabilities, hindering the development of AI-driven solutions. They must either invest in training or partner with external entities to bridge the gap.
• Recruitment and Retention of AI Talent: AI talent is scarce, and it’s a growing problem for financial institutions in attracting and retaining professionals. This is even more challenging in GenAI, with a high demand for advanced skills.
• High Costs and Time for Upskilling: Training existing employees in advanced AI and GenAI technologies is costly and time-consuming.
• Risk of Project Delays or Failures: Artificial intelligence is a relatively new field that faces significant challenges, such as delays or failures due to the need for large datasets, advanced computing infrastructure, and iterative model development and testing.
• Complex Vendor Management: Managing multiple vendors or freelancers for AI projects can be complex and time-consuming because of the diversity of skills required for projects.
• Integration with Existing Systems: Legacy systems, data silos, and compatibility issues can significantly slow down AI adoption and limit its effectiveness.
• Data Privacy and Security: AI and GenAI applications often rely on vast amounts of data, raising concerns about privacy and security. Financial institutions must ensure that their AI initiatives comply with data protection regulations and are secure against cyber threats.

You see now that navigating the AI implementation landscape in the financial sector is tricky, to say the least. The key to success is a team with diverse skills and roles that can cater to the various demands of the project.

Imagine a situation where a financial institution wants to use AI for different purposes, such as data analysis, predictive maintenance, or improving customer interactions. To achieve this, the institution decided to collaborate with a team of experts carefully selected to cover essential roles, from Data Scientists and Software Engineers to Business Analysts, among others. Each team member brings their unique expertise to the table, from machine learning and deep learning to cloud service management.

Now imagine that the project requires the collective capabilities of this team to cover a broad spectrum, including but not limited to Azure’s suite of services like Azure Databricks and Azure ML and programming languages such as Python and Java.

This skill set covers everything AI projects need, from idea generation to system maintenance and analytics. The team works on several critical areas:

• Ideation: They brainstorm to identify potential AI applications that align with the institution’s strategic goals.
• Integration and Implementation: They ensure seamless integration of AI technologies into existing workflows, enhancing efficiency and productivity.
• Platform Adoption and Maintenance: They manage the adoption of AI platforms, ensuring these systems are optimized and maintained effectively.
• Business Case Development: They assist in defining the business value of AI initiatives and supporting decision-making processes.

This is a hypothetical situation. However, we managed to create a quite detailed brief outlining the requirements for an AI-specialized outsourcing company. And if you’re looking for one just like that, we have a solution for you:

Scalo is a leading provider of AI services in the finance industry, offering a blend of top-notch expertise and cutting-edge technology to drive innovation forward. We specialize in Azure, AI, and Machine Learning, which gives us a strong position for modernizing the finance world.

By simplifying complex data management and transforming data into strategic insights, we make sure you maximize your economic outcomes while keeping the implementation processes streamlined.

So, if you want to have tech wizards at your side to ensure you stay ahead in the fast-paced finance sector, we are your team. Contact us, and let’s discuss how AI can help your business grow.