How technology is impacting gaming compliance programs

Discover how data analytics and AI are reshaping compliance in gaming, enhancing risk management and operational efficiency.

In brief 

• What role do data analytics and AI play in improving compliance processes within the gaming industry? 
• What challenges do gaming organizations encounter when adopting new compliance technologies? 
• How are gaming organizations adapting their compliance programs to incorporate advanced technology solutions effectively?

During the recent Global Gaming Expo (G2E), EY moderated the Technology’s Impact on Gaming Compliance Programs panel, focusing on various topics impacting compliance operations now and in the future.

The audience participated in a series of polling questions for real-time feedback regarding the uses and implementation of technology within their compliance programs. The results from audience participation highlighted the heavy reliance on data analytics, the current use of AI and future AI trends and challenges in technology implementation. As organizations continue to look for ways to implement technology solutions to standardize compliance areas related to AML, fraud and risk management, it is important to understand the current technology environment, industry needs and future state considerations.

Data analytics considerations

For example, data analytics can build risk scoring workflows for dynamic processing of large amounts of transactions, in discrete or continuous manners. Transactions from multiple sources can be connected and transformed into usable models, where compliance teams can set thresholds and weights for risk testing that allow for dynamic adjustment. Results from the risk assessment can be displayed visually, such as in heatmaps, which graphically chart key risk considerations.

For example, natural language processing can be leveraged for automated transaction monitoring components, including alert generation and SAR preparation. Companies should consider the inputs to their machine learning models, verifying the training data sets are accurate. Otherwise, outputs will only be as good as the quality of the input data. The increased use of data analytics requires additional resources who can understand and interpret the data to effectively manage compliance risks.

AI considerations

Operators are facing challenges, including confirming the accuracy of AI results and reaching a level of comfort when presenting results to regulators. Compliance teams should prioritize assessing quality assurance when reviewing the results of generative AI. Generative AI can “hallucinate” results by providing false or misleading information. AI solutions will require reviews of both the inputs to a model as well as the accuracy of the results. When responding to regulatory requests, it is crucial to maintain accountability and explainability of decisions made. Use of AI models will require both knowledge of the relevant compliance and risk needs and technical knowledge to assess accuracy and factual representation of results.

Of the respondents who noted that they were leveraging AI, the most common uses were for identity verification or facial recognition, behavioral analysis and chatbots. There are several compliance products that use AI to assist in ID verification. These tools help to determine if an ID is authentic or has been manipulated and can verify the authenticity of a customer’s ID in real time. These tools combine KYC-compliant ID verification with automatic AML screening to inform AML risk decisions, while limiting the friction created with patron onboarding.

Technology implementation challenges and opportunities

Respondents were asked to contribute to a word cloud of the greatest technology challenges and opportunities that they face. The majority of respondents noted that cost and integration are the biggest challenges with new technology adoption and implementation.

When choosing to implement a new technology solution, companies should first seek to harness internal data sources and technology when evaluating future needs. For example, many cloud platforms have extensive toolsets that may already be included in a company’s cloud service subscriptions that go underutilized. Some may include text extraction tools with optical character recognition (OCR) for processing PDF documents.

Having robust change management processes are also essential to effectively assessing the impacts of technology implementation. Effective change management programs include sufficient project planning processes (e.g., establishing goals, parameters and an implementation schedule) and establishing consistent feedback mechanisms throughout the phases of implementation and go-live.

Respondents indicated that the greatest opportunity to leverage technology in the coming years are in risk management, ID verification and transaction monitoring. Given the critical roles these areas play as part of compliance programs, it is important to leverage technologies where possible to enhance these control measures.

Elise Lebourg and Morgan DaRe also contributed to this article.