Sportradar Integrity Services Nothing’s new under the sun in match-fixing
As we head into 2025, the integrity space in sports betting is poised to benefit from continued advances in AI and data analytics, making detection and prevention of match-fixing more efficient than ever. As the data ecosystem continues to grow and technology evolves, Sportradar’s approach will adapt explains Jack Kennedy, VP Anti-Match-Fixing, with a focus on not only improving betting market monitoring but also expanding its role in supporting intelligence gathering and enhancing regulatory compliance across the industry.
How is the integrity space changing as we head into 2025?
Te world of match-fixing is constantly evolving. Te challenge is to stay on top of the latest trends to ensure that, as a technology company, we are continually moving forward and have the correct data infrastructure and resources to keep up with the pace of advancement. Nothing's new under the sun in match-fixing. We often see the same characters popping up throughout the years, but adapting to changes in the landscape is always important. As we'll likely touch on later, there is an increased reliance on betting data from operators, so we need to continue building out those relationships and the information flow is key. Te biggest challenge is that, because of the confidentiality of ongoing investigations, sometimes it's difficult for operators to get feedback from sporting federations and that creates difficulties. Tese cases take time, they’re complex and you've got to ensure operators are understanding of the fact that their information being used to benefit these cases is behind closed doors for positive change.
What’s the ratio of suspicious activity detection that’s now analysed by AI as opposed to human oversight?
It's not so much that these matches are detected by AI and these matches are detected by humans. Te real reason that the technology is in place is for the AI component to support human analysis. We monitor hundreds of thousands of matches per year and the AI model is there to separate the wheat from the chaff as it were and identifies the small subset of potentially suspicious matches that require further human analysis. Our Universal Fraud Detection System
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(UFDS), a system we built at Sportradar, processes data taken from a wide range of operators, from major to local operators. Te system ingests all the betting data, and the AI model is there to identify suspicious betting activities - be it pre-match markets with a large odds change, increased turnover on a certain betting market, or in live betting markets, increased turnover or odds deviation from expected levels. Te AI and alerting system are there to essentially flag out matches that require further human analysis.
Te AI model has been a terrific addition it's constantly learning, improving and ingesting data from multiple sources. It does things that humans can do at a much greater speed, but there is always going to be a need for human analysis. Strong betting doesn't always point to suspicious betting. Tere are various reasons for strong betting. For example, as we came out of the pandemic and live sports resumed there were situations where large swathes of players would be ruled out. Tere are other examples when a team has been spotted travelling to a match at the airport and several star players aren't travelling. Tat have even been situations where a team has started 10 players and been forced to play a second goalkeeper outfield. Tese are all instances that require human analysis to differentiate between what is legitimate betting and what is suspicious betting.
Does every global sports activity receive the same level of scrutiny, or is the computational power of the UFDS more targeted than that?
One of the greatest things about the machine learning model and the increased use of AI in our monitoring is the ability to monitor in a horses for courses manner. Each sport has its own betting dynamics.
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