6 Ways to Boost Your ROI on AI  

If your Artificial Intelligence (AI) goals seem out of reach, think again. All you need is the right strategy. According to a February 2022 survey by AI Forum, almost 70% of financial services professionals surveyed reported good, strong, or outstanding Return on Investment (ROI) on their AI investment. How did they do it?

Increasing ROI on AI for financial services starts with the right data, strategy, and project-specific key performance indicators (KPIs). Learning how to bring these critical areas together is key to gaining successful ROI from your AI deployment. Anything less will set organizations up for failure.

Common Deployment Approaches

With more at stake to meet data privacy, security, and regulatory requirements, financial services companies must determine the best AI deployment approach to meet their needs. In the AI Forum survey, financial professionals highlighted the following common approaches for their AI projects:

• Buy an end-to-end solution—15%
• Build your own solution—29%
• Take a hybrid approach—53%

Why AI Deployments Fail

The reason financial services companies don’t achieve successful ROI on their AI deployments isn’t because of the approach they use. It’s due to the problems surrounding their data.

Data quality

From online forms to customer data inputs, data entry points are the start of data quality challenges. Most of these problems are caused by human error. Despite validation efforts, human error persists as employees often enter dummy text to “fool” the system. They don’t always realize the full extent of the quality issues until they combine their data with data from various customer systems.

Data management

Companies often create data lakes to manage and process their data, only to find them becoming a dumping ground of raw data. With no single view of the data, data scientists and engineers don’t get the format they need for their purpose without doing manual coding or using extract-transform-load (ETL) tools. Despite all their efforts in formatting the data, they can’t reuse the format for other tasks, requiring them to repeat the same labor-intensive processes each time.

Organizational silos

Segregating data into organizational silos hinders visibility and clarity across departments and divisions within the same financial services company. It creates a narrow view of customers, dismissing their broader relationships. Efforts to break down these silos with customer data platforms (CDPs), customer relationship management (CRM) systems, and data lakes lack the sophisticated technology to define and visualize relationships in the data.

Industry regulation and compliance

Industry regulations for financial services companies—such as Know Your Customer (KYC) and financial crime compliance—ensure an accurate view of finances and business operations. And more are on the way for AI. Starting in 2022, companies in the US and UK—with the EU just behind them—must demonstrate transparency and oversight into the use of algorithms and data sets for AI projects. Meeting regulations is difficult for financial companies without effective data quality and management processes in place. Without such processes, they risk big-time fines that can cut funds and resources for future AI projects.

6-Point AI Deployment Strategy for Successful ROI

Data aside, achieving successful outcomes from your AI projects demands the right strategy. It requires taking in a big-picture view to understand where, how, and why to implement AI across the enterprise.

While historically, lackluster ROI in AI has troubled the financial services sector, 68% of financial services professionals have indicated good or even strong ROI from their AI initiatives due to the implemetation of a successful AI deployment strategy.

To follow suite, start by following this six-point strategy for a successful ROI of your AI project.

1. Find the right problems

Investing in AI for financial services isn’t a one-and-done project. Rather, it entails identifying problems across your company that can benefit from AI. For example, it can mean opportunities for:

• Reducing time-intensive and resource-intensive costs
• Offering better customer experience
• Providing advanced fraud detection
• Improving the ability to meet regulatory compliance
• Refining loan and credit decisions
• Automating investment research and guidance

These challenges occur in various areas across the organization and not necessarily at the same time. By understanding the problems to address up front, you’ll know what data you need and which sources to pull from.

2. Define your business use case

Once you know the problem(s) you want to solve using AI, decide on a business use case. Financial services companies often use AI for:

• Business development
• Compliance, surveillance, and anti-money laundering
• Credit risk decisioning
• CRM, customer service, and customer insight
• Financial crime and fraud detection
• Governance and risk
• KYC and customer onboarding
• Operational optimization

Defining your use case requires meeting with key stakeholders from your business operations, technology, compliance, and data science teams to identify:

• Repetitive, time-consuming tasks that rely on data and rules
• Critical business information hidden in your data that you need to access
• Areas where human decision-making can create context around data
• Unstructured data that’s used, accessed, and served
• Ways your internal and external customers use enterprise applications

By getting buy-in from all departments, enterprises can build a solid foundation for their use case and show upper level management that the company is on track to meet their AI goals.

3. Clean up your data

While this step might be third in this list, it just might be the most critical foundational step for achieving AI success. AI runs on big data to generate business and customer insights. But to be successful, you need to be data-ready so you can create a single, connected customer view to create context around your data. Data lakes, data silos, CDPs, and CRMs don’t allow for this level of quality to enable AI decision-making.

Data readiness requires:

• Entity resolution to connect your data from across your enterprise
• Network generation to build a dynamic, big-picture view of your internal and external data
• Advanced analytics to build analytic models based on real-world experience
• Network visualization to create context around your data for better decision-making

By establishing this level of data quality across your enterprise, leaders can make more meaningful and trusted decisions.

4. Address security, privacy, and regulatory issues

Taking on AI means your project must meet regulatory requirements, particularly for data security and privacy. Brushing over these areas puts your entire enterprise at risk for a data breach, lost or stolen data, and, potentially,  millions of dollars in fines by industry and government regulators.

To address these challenges, enterprises must:

• Create a big-picture view of your data through entity resolution and network generation.
• Establish the necessary data protection measures across the enterprise:
  • Minimizing human error
  • Fine-tuning data handling procedures
  • Securing your technical infrastructure
  • Applying the right mix of security tools, skills, and expertise
• Stay current on industry and federal regulations to uphold data security and privacy.

By safeguarding your data this way, you can guard against fraudulent activities and maintain your data confidentiality, integrity, and privacy.

5. Implement your program one step at a time

AI projects often fail because companies try to do too much all at once by rushing to deployment. Successful AI deployment requires careful planning and the right mix of resources:

• Build a cross-functional group of representatives from your data science, business analyst, business professional, development, data protection, security, and IT teams
• Identify the skills and resources you need and have, then address and fill any gaps
• Make sure everyone knows their roles and expectations for meeting the project’s goals
• Develop a timeline that keeps the implementation moving smoothly
• Allow for open communication and transparency along the way

With this basic guide, your AI deployment will get off to a good start and achieve successful deployment.

6. Measure the benefits from your AI deployment

The true test of successful ROI—hard ROI—on your AI deployment is in your project’s KPIs. For example, you might evaluate:

• Time savings earned by automating time-consuming, manual, or cognitive tasks
• Productivity gains by investing in AI for more effective and efficient processes
• Cost savings realized by implementing AI
• Break-even point of when the cost savings equals your investment into the project and the time to reach that point
• Revenue growth from new products and services enabled by AI

Also, consider the soft ROI, such as:

• Improved productivity
• Better customer experience
• Skills retention
• Greater agility in taking on new opportunities and challenges

By measuring both hard and soft ROI, you can more accurately gauge the success of your AI deployment.