Financial institutions worldwide are forecast to spend more than US$66.2 billion on fraud prevention by 2031, an 82.9% increase from the US$36.2 billion expected in 2026, according to Juniper Research. The rise reflects growing investment in cross-industry intelligence sharing as criminal networks exploit gaps between banks, payment providers and digital platforms.
Juniper’s Banking Fraud Prevention Market 2026-2031 study covers 60 countries and includes Asia-Pacific markets such as China, Hong Kong, Japan, South Korea, Taiwan, India, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. It forecasts that banking fraud will reach 2.2 billion transactions by 2031, fuelled by AI-enabled attacks and increasingly sophisticated social engineering.
Asia’s fraud challenge
The findings are particularly relevant to Asia, where rapid growth in instant payments, digital wallets, e-commerce, and cross-border remittances has expanded both financial inclusion and the fraud attack surface. Juniper’s research includes country-level data and forecasts for major Asian markets, allowing banks and fintechs to assess fraud incidence, prevention spending and adoption by market.
The report argues that individual institutions often cannot see the full risk picture. A bank may understand risks associated with its own customers but lack visibility of the beneficiary receiving a payment, making money-mule networks harder to identify before funds move through the financial system.
“Banks may understand the risks associated with their own customers but often lack visibility of the beneficiary receiving the payment,” said Shane O’Sullivan, the report’s author.
“Sharing accurate and up-to-date intelligence across institutions will therefore be essential to identifying mule networks earlier and preventing fraudulent funds from moving further through the financial system.” Shane O’Sullivan
Troy Nyi Nyi, SVP & GM, APAC at SEON, adds that money-mule networks are becoming more coordinated, with fraudsters using multiple accounts and payment channels to move funds while attempting to avoid detection.
The challenge for financial institutions, he says, is that suspicious activity does not always begin with the transaction itself. It can start much earlier, through account creation, device activity or the use of identities linked to other suspicious accounts.
Troy Nyi Nyi
“Banks should therefore work closely with fraud detection partners to look beyond individual transactions and understand the relationships between accounts, devices and payment activity to identify potential mule networks before funds are moved.” Troy Nyi Nyi
Shared intelligence gains ground
Juniper identifies collaboration as central to the next phase of fraud prevention. In July 2026, UK fraud-prevention organisation Cifas partnered with the Global Signal Exchange to connect intelligence across banking, telecommunications, retail and technology platforms—an example of the cross-sector data sharing the report expects to become more important.
The research recommends combining mobile-network intelligence with behavioural, identity and payment signals to improve detection. Vendors able to provide visibility across financial institutions are likely to be better placed as banks seek to identify fraudulent beneficiaries earlier and strengthen payment decisions.
Nyi Nyi notes that as digital payments and cross-border remittances grow, financial institutions need to look at fraud signals across the customer journey rather than assessing transactions in isolation.
He asserts that signals such as device activity, IP and geolocation data, behavioural patterns, identity information and transaction history can each indicate risk; when assessed together, they can help uncover connections between seemingly unrelated accounts and transactions, including activity that may point to money-mule networks.
“This also means bringing fraud prevention and anti-money laundering controls closer together, so financial institutions can identify suspicious patterns earlier, make more informed decisions and avoid creating unnecessary friction for legitimate customers,” he continues.
For Asian financial institutions, that implies closer cooperation not only among banks but also with telecom operators, payment networks, e-commerce platforms and regulators. The report covers banks and credit unions, fintechs, investment companies, and lending companies, reflecting the breadth of institutions now exposed to fraud risk.
“Money-mule networks exploit the gaps between institutions,” observes Subhashish Bose, director of global advisory at BioCatch. He explains that “one bank sees the customer sending money, while another may see the account receiving it. Sharing real-time signals across banks pre-transaction helps identify fraudulent beneficiaries, and allows the sending side bank to stop the scam payment from going through.”
AI changes the economics
Juniper says AI is reshaping the market on both sides of the fraud equation. Financial institutions are deploying machine learning, behavioural analytics, transaction monitoring and real-time intelligence to counter AI-enabled fraud, while also confronting challenges such as false positives.
“While shared intelligence starts from the accounts that have already been flagged, the harder problem is the mule account that hasn’t been flagged yet,” stressed Bose.
Subhashish Bose
“Behavioural and device signals, such as how and what type of sessions are operated and whether the device or account is linked to other users, let a bank spot mule behaviour proactively and long before funds move on.” Subhashish Bose
For Asia-Pacific banks and fintechs, fraud prevention is becoming a shared-data problem rather than purely an in-house controls exercise.
As Bose’s comments suggest, shared intelligence alone is not enough: institutions also need behavioural and device-level signals to spot mule accounts not yet flagged. Those that invest early in interoperable intelligence, real-time detection and cross-sector partnerships may be better positioned to protect customers and preserve trust in digital finance.
