AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027

• Market Dynamics: Detailed assessment of how different trends are leading to greater adoption of AI and machine learning within the financial fraud detection and prevention space, such as the need for greater scalability, increases in digital transactions, and ongoing fraudster innovation.
• Key Takeaways and Strategic Recommendations: This provides actionable recommendations and vital key takeaways, allowing vendors in this market to refine their strategies.
• Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 17 AI in financial fraud detection and prevention vendors:
  • ACI Worldwide
  • Cybersource
  • Experian
  • Featurespace
  • Feedzai
  • FICO
  • GBG
  • Kount, an Equifax Company
  • LexisNexis Risk Solutions
  • Microsoft
  • NICE Actimize
  • NuData Security
  • Pelican
  • Riskified
  • SymphonyAI Sensa
  • Temenos
  • Vesta
• Benchmark Industry Forecasts: 5-year forecasts for the spend on AI-enabled financial fraud detection and prevention platforms, as well as the number of digital commerce transactions screened by AI versus rules-based systems, and the time and cost savings from the use of AI in financial fraud transaction monitoring. Data is also split by our 8 key regions and the 60 countries listed below:
  • North America:
    • Canada, US
  • Latin America:
    • Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Uruguay
  • West Europe:
    • Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK
  • Central & East Europe:
    • Croatia, Czech Republic, Hungary, Poland, Romania, Russia, Turkey, Ukraine
  • Far East & China:
    • China, Hong Kong, Japan, South Korea
  • Indian Subcontinent:
    • Bangladesh, India, Nepal, Pakistan
  • Rest of Asia Pacific:
    • Australia, Indonesia, Malaysia, New Zealand, Philippines, Singapore, Thailand, Vietnam
  • Africa & Middle East
    • Algeria, Egypt, Israel, Kenya, Kuwait, Nigeria, Qatar, Saudi Arabia, South Africa, United Arab Emirates

1. What will the total value of the AI financial fraud detection and prevention market be in 2027?
2. How important is explainability where AI is used to prevent financial fraud, and how can this be facilitated?
3. How will greater AI use impact financial fraud?
4. Where are the biggest opportunities for vendors in the AI financial fraud detection market?
5. Who are the leading vendors of AI financial fraud detection platforms?

Included in the Juniper Research Competitor Leaderboard: ACI Worldwide, Cybersource, Experian, Featurespace, Feedzai, FICO, GBG, Kount, an Equifax Company, LexisNexis Risk Solutions, Microsoft, NICE Actimize, NuData Security, Pelican, Riskified, SymphonyAI Sensa, Temenos, Vesta.
 
Mentioned: Accertify, Accuity, Acuris, Adidas, Air Europa, Aldo, Alipay, Amadeus, AT&T, Auchan, Azul Systems, Banca Sella, Barclaycard, Betfair , BioCatch, BlueSnap, BNP Paribas, BNY Mellon, Braintree, Bukalapak, Bvaccel, Canada Goose, Capgemini, CARDNET, Cayan, CellPoint Digital, Chargebacks911, Checkout.com, Citrus Pay, Cloudera, Coneta, Coop, Credorax, CSI, Data Robot, Datastax, Deloitte, Diebold Nixdorf, Discover, eBay, EgyptAir, Elevon, Emailage, Entersekt, Equifax, Ethoca, Etisalat, Eversheds, Evo Payments, Eway, Experian, FedNow, Finxact, First Data, Fiserv, FreedomPay, Gemalto/Thales, General Insurance , GPG (Global Payroll Gateway), Hay, HP, HSBC, IBM, ID R&D, IDology, ING, Innovalor, Invation, iovation, Jack Henry & Associates, JPMorgan Chase, Karlsgate, Last Minute, Lego, Linktera, Magneto, Mastercard, Mattel, Moku, NASDAQ, NetSuite, NorthRow, OpenWrks, Oracle, Oracle Commerce, PassFort, PayPal, Pilot Flying J, Plaid, PLDT, Prada, Protiviti, Red Hat, RELX, Revelock, Ring, RSA, Sage, Salesforce, Santander Bank, SAP, Sayari Labs, Sekura, SEON, Shopify, Singapore Airlines, Sionic, Socure, Solarisbank, Sparkling Logic, SPhonic, State Bank of India, Stripe, Stuzo, Swedbank, TCH, TCS (Tata Consultancy Services), Telcel, ThreatMetrix, T‑Mobile, TransUnion, UBS, UnionPay, United Colours of Benneton, Venmo, VeriFone, Visa, Visualsoft, Wells Fargo, Wendy’s, Westpac, Whitepages Pro, Wish , Zelle, Zilch, Zooz.
 

Key Market Forecast Splits

The AI in Financial Fraud Detection forecast suite provides data splits for the following metrics:

• Spend on AI-enabled financial fraud detection and prevention platforms
• The number of digital commerce transactions screened by AI-enabled systems
• The number of digital commerce transactions screened by purely rules-based systems
• Time savings from the use of AI in financial fraud transaction monitoring
• Cost savings from the use of AI in financial fraud transaction monitoring

Geographical splits: 60 countries
Number of tables: 23 tables
Number of datapoints: Over 10,400 datapoints

harvest: Our online data platform, harvest, contains the very latest market data and is updated throughout the year. This is a fully featured platform; enabling clients to better understand key data trends and manipulate charts and tables, overlaying different forecasts within the one chart - using the comparison tool. Empower your business with our market intelligence centre, and get alerted whenever your data is updated.

Interactive Excels (IFxl): Our IFxl tool enables clients to manipulate both forecast data and charts, within an Excel environment, to test their own assumptions using the interactive scenario tool and compare selected markets side by side in customised charts and tables. IFxls greatly increase a clients’ ability to both understand a particular market and to integrate their own views into the model.
 

The global business spend on AI-enabled financial fraud detection and prevention platforms will exceed $10 billion globally in 2027; rising from just over $6.5 billion in 2022.

• Growing at 57% over the period, we predict that as fraudsters become more sophisticated in their attacks, merchants and issuers will become more adept at utilising highly advanced AI-enabled fraud detection methods to combat crime. The ability of AI to recognise fraudulent payment trends at scale is critical to provide improved fraud prevention.
• Cost savings from AI deployment will be critical to taking system use beyond regulatory compliance and providing a genuine return on investment on fraud prevention services, with improving models and greater data access creating a virtuous circle of improvement.
• We forecast growth of 285%, with cost savings reaching $10.4 billion globally in 2027, from $2.7 billion in 2022.
• By leveraging AI, businesses can shift their fraud management resource to where it matters, investigating the key issues, rather than dealing with endless false positives, boosting efficiency.
• Additionally, AI is increasingly standard within financial fraud prevention services; making differentiation a challenge. Therefore, vendors should focus on access to transaction and trends data, as gaining the best level of network intelligence will allow businesses to benefit from fraud information from beyond just their own transactions, significantly improving fraud prevention. Vendors should make partnerships with third parties, such as credit bureaus and payment networks, to improve their data coverage.