top of page

Our POV: 12 predictions for AI in financial services for 2024

As we enter the new year, sharing my take on twelve bold predictions for AI in the financial services sector:

1. Enhanced Customer Service: AI will revolutionize customer service by leveraging advanced natural language processing and machine learning algorithms to provide personalized assistance, leading to an estimated 25% increase in customer satisfaction scores.

2. Risk Assessment and Fraud Detection: AI-powered risk assessment tools will see a 30% reduction in false positives, improving accuracy and efficiency in identifying potential risks and fraudulent activities, saving financial institutions an estimated $10 billion globally.

3. Automated Trading: AI-driven trading algorithms will account for over 40% of all financial trades, resulting in an average annual return that outperforms traditional human-managed portfolios by 15%.

4. Regulatory Compliance: AI systems will streamline compliance processes, reducing the time and cost of regulatory adherence by 20%, while also ensuring a 30% decrease in compliance-related errors.

5. Personalized Financial Planning: AI will enable more accurate and personalized financial planning, leading to an estimated 35% increase in the number of individuals actively saving and investing for their long-term financial goals.

6. Algorithmic Credit Scoring: AI-powered credit scoring models will result in a 15% increase in approved loans for creditworthy individuals who were previously overlooked by traditional credit scoring methods.

7. Predictive Analytics for Lending: AI will be used to improve lending decisions, resulting in a 25% decrease in default rates and a 20% increase in the availability of credit for small and medium-sized enterprises.

8. AI-Powered Chatbots: Chatbots leveraging AI will handle over 70% of customer inquiries, providing 24/7 support and reducing customer service costs by an average of 40% for financial institutions.

9. Cybersecurity Enhancement: AI will bolster cybersecurity efforts, reducing the average time to detect and respond to cyber threats by 50%, leading to an estimated 70% decrease in successful cyber attacks on financial institutions.

10. Operational Efficiency: AI-driven process automation will lead to a 30% increase in operational efficiency, resulting in an estimated $15 billion in cost savings across the financial services industry.

11. AI in Wealth Management: Wealth management firms that adopt AI-driven tools will see a 25% increase in assets under management, as AI assists in providing more tailored investment advice and portfolio management.

12. Expanded AI Adoption: Overall, there will be a 40% increase in the adoption of AI technologies across various financial services, with an estimated 60% of financial institutions utilizing AI for at least one core business process.

These predictions are based on the increasing integration of AI technologies, historical trends, and the growing impact of AI on the financial services sector.

If a lender or mortgage servicer fails to embrace digital transformation and AI, they may miss out on several opportunities and advantages. Here are ten key things they might miss:

1. Enhanced Customer Experience: By not adopting digital and AI technologies, they may lose the chance to provide personalized and convenient experiences to customers, leading to lower satisfaction levels.

2. Streamlined Processes: Digital transformation and AI can automate and streamline various mortgage processes, reducing manual efforts, minimizing errors, and improving efficiency. Without these advancements, the lender may struggle with time-consuming and error-prone manual tasks.

3. Improved Risk Management: AI-powered analytics can help identify potential risks and fraud in loan applications, reducing the chances of defaults and financial losses. Failing to utilize these tools may result in higher risk exposure.

4. Competitive Advantage: Lenders who embrace digital technologies gain a competitive edge by offering faster loan processing, online self-service options, and innovative features. Without these, they may struggle to keep up with tech-savvy competitors.

5. Cost Savings: Automation and AI can significantly reduce operational costs by streamlining processes, eliminating manual tasks, and improving resource allocation. Without these efficiencies, the lender may incur higher expenses.

6. Data-Driven Insights: Digital and AI tools enable lenders to analyze vast amounts of data, gaining valuable insights into customer behavior, market trends, and risk assessment. Failing to leverage these insights may result in missed opportunities for informed decision-making.

7. Scalability: Digital platforms facilitate scalability, allowing lenders to handle a higher volume of loan applications without compromising quality. Without this scalability, lenders may struggle to accommodate growth and expand their customer base.

8. Regulatory Compliance: Digital transformation can help lenders stay compliant with evolving regulations by automating compliance checks and documentation. Neglecting these advancements may lead to compliance issues and penalties.

9. Predictive Analytics: AI-powered algorithms can provide lenders with predictive models for assessing creditworthiness, market trends, and identifying potential defaults. Without these tools, lenders may struggle to make accurate predictions and informed decisions.

10. Innovation and Adaptability: Digital transformation fosters a culture of innovation, allowing lenders to adapt to changing customer expectations, market dynamics, and emerging technologies. Without embracing these changes, lenders may become stagnant and less responsive to market demands.

These missed opportunities highlight the importance of digital transformation and AI adoption in the mortgage industry, as they can significantly impact a lender's competitiveness, operational efficiency, and customer satisfaction.

Here's my quarterly breakdown of detailed strategies to implement AI in a financial services lender/mortgage servicer for 2024:

Quarter 1: January - March 2024

1. Assessment and Planning:

   - Conduct an in-depth assessment of existing processes and technologies to identify areas where AI can bring the most value, such as credit scoring, risk assessment, and customer service.

   - Form a cross-functional team involving IT, data science, risk management, and business operations to oversee the AI integration process.

2. Data Readiness:

   - Focus on data preparation and quality improvement to ensure that the AI algorithms have access to accurate and comprehensive data for training and validation.

   - Invest in data governance and quality assurance processes to maintain the integrity of data throughout its lifecycle.

Quarter 2: April - June 2024

3. Pilot AI Applications:

   - Select a specific area, such as credit scoring or customer service, to pilot AI applications. This could involve implementing a machine learning model for more accurate credit risk assessment.

   - Set clear KPIs to measure the impact of AI implementation, such as reduced processing time, improved accuracy, or enhanced customer experience.

4. AI Talent Acquisition and Training:

   - Hire data scientists, machine learning engineers, and AI specialists to build in-house expertise in AI application development and maintenance.

   - Provide training programs for existing employees to ensure a widespread understanding of AI technologies and their potential impact on day-to-day operations.

Quarter 3: July - September 2024

5. Scaling AI Solutions:

   - Based on the results from the pilot phase, scale up successful AI applications to cover a broader range of operations, such as automating repetitive tasks in mortgage servicing or enhancing customer interaction through AI-powered chatbots.

   - Establish partnerships with AI vendors or service providers to access specialized AI tools and technologies if needed.

6. Regulatory Compliance and Ethical AI:

   - Develop and implement AI governance frameworks to ensure compliance with relevant regulations and ethical AI practices, particularly in sensitive areas such as credit assessment and risk management.

   - Collaborate with legal and compliance teams to ensure that AI applications meet the required regulatory standards and ethical guidelines.

Quarter 4: October - December 2024

7. Continuous Improvement and Innovation:

   - Establish a feedback loop to gather insights from end-users and stakeholders to continuously improve AI applications and their relevance to the business.

   - Explore new AI opportunities, such as using natural language processing for mortgage document analysis or predictive analytics for customer retention.

8. Measurement and Reporting:

   - Regularly assess the impact of AI implementation by tracking KPIs and metrics related to operational efficiency, customer satisfaction, risk mitigation, and financial performance.

   - Generate comprehensive reports to communicate the value of AI to internal stakeholders and external partners.

By following these quarterly strategies, the financial services lender/mortgage servicer can systematically integrate AI into its operations, ensuring a smooth transition and maximizing the benefits of AI technologies.

Here are the key steps in forming a business case and calculating Return on Investment (ROI) for AI investments in 2024:

Step 1: Identify Business Objectives and Challenges

- Understand the specific business objectives that the AI investment aims to address, such as improving operational efficiency, enhancing customer experience, or reducing risk in lending processes.

- Identify the current challenges and pain points within the organization that AI can potentially solve, such as manual, time-consuming processes, high error rates, or inefficiencies in decision-making.

Step 2: Define Key Performance Indicators (KPIs)

- Establish clear and measurable KPIs that align with the identified business objectives, such as reducing processing time, increasing accuracy, improving customer satisfaction scores, or lowering operational costs.

- Ensure that the KPIs are quantifiable and directly linked to the expected outcomes of the AI investment.

Step 3: Assess Current State and Future State

- Evaluate the current state of operations, technology infrastructure, and data capabilities to understand the baseline performance metrics and costs.

- Define the future state that the organization aims to achieve with AI implementation, outlining the expected improvements in performance, cost savings, or revenue generation.

Step 4: Cost-Benefit Analysis

- Estimate the costs associated with AI implementation, including expenses related to software and hardware acquisition, hiring or training AI talent, data preparation, and ongoing maintenance.

- Conduct a thorough analysis of the potential benefits, such as increased productivity, reduced error rates, improved customer retention, and competitive advantages.

Step 5: Risk Assessment

- Identify and assess potential risks associated with AI implementation, such as regulatory compliance, data privacy concerns, technology integration challenges, or resistance to change within the organization.

- Develop risk mitigation strategies to address these concerns and ensure a smooth implementation process.

Step 6: Formulate the Business Case

- Compile the findings from the previous steps into a comprehensive business case document that outlines the rationale for AI investment, including the identified business objectives, KPIs, cost-benefit analysis, and risk assessment.

- Clearly articulate the expected outcomes and benefits of AI implementation, along with a realistic timeline for achieving results.

Step 7: Calculate ROI

- Quantify the expected financial returns from the AI investment by calculating the projected ROI. This involves comparing the anticipated benefits (such as cost savings, revenue growth, or efficiency gains) against the total investment costs.

- Use relevant financial metrics, such as net present value (NPV), internal rate of return (IRR), or payback period, to assess the financial viability of the AI investment.

Step 8: Continuous Monitoring and Adjustment

- Establish a framework for continuous monitoring of the AI implementation's performance against the set KPIs and ROI projections.

- Be prepared to make adjustments to the business case and ROI calculations based on real-time data and feedback as the AI solution is deployed and integrated into the organization's operations.

By following these key steps, organizations can develop a solid business case for AI investments and accurately evaluate the potential ROI, laying the groundwork for successful AI implementation and realizing tangible business benefits.

Matt Slonaker

Founder & CEO of M. Allen 

(M) 972.740.4300

85 views0 comments

Recent Posts

See All


bottom of page