Trained to detect, equipped to deflect: How BFSI contact centres enable organisations to stop scams at source
The rise of digital banking and real-time payments has completely transformed how Australians bank. The convenience, however, brought forth risks for the Banking, Financial Services, and Insurance (BFSI) sector. For real-time payments, the window that banks get to detect fraud is so narrow that fraudsters exploit it greatly. According to an Australian government website run by the Australian Competition and Consumer Commission (ACCC), over 24,000 phishing scam cases have been reported in the first two months of 2025 alone. The rise of AI has also impacted fraud, so today, the anatomy of fraud looks much more convoluted.
Anatomy of banking fraud in Australia
While banking fraud witnessed a dip in numbers, the democratisation of AI made scams in Australia more complex. Synthetic identity fraud is the prime example of AI-charged fraud. It has always existed, but the skills (for the lack of a better word) of spending hours fabricating legitimate IDs and documents are no longer needed.
AI’s role in changing scams has wreaked havoc on the banking system. A study conducted by an open-access archive on AI spear phishing campaigns evaluated the effectiveness of AI-generated spear phishing emails and other scams. AI-generated phishing messages received a click-through rate of 50%. Most shockingly, the campaign was also cost-efficient, enabling scammers to target fewer Australians while causing a higher financial loss.
This showcases that AI has not only brought forth unique challenges but also the capabilities to scale existing fraud. Banks and financial institutions are also leveraging AI for fraud detection, and it has become an integral part of customer onboarding, document tampering detection, real-time transaction monitoring, and more.
Why traditional authentication methods fall short
The question the BFSI industry needs to address is: Can legacy verification systems, such as static passwords and security questions, stand up against this fraud landscape? Much personal information can be harvested from social media and breached databases, making security questions a fragile framework. Text-based one-time password (OTP) authentication was considered the next best thing, but the surge in SIM-swap fraud has exposed this method.
Legacy methods often treat authentication as one-size-fits-all, leaving no room for contextual insights. The same can be said for rule-based systems. To detect fraud, especially banking fraud in Australia, context is crucial. Location, device fingerprint, transaction history, and behavioural insights need consideration.
The strategic role of BFSI contact centres in source-level fraud prevention
BFSI contact centres serve as the first line of defence against financial fraud by verifying the caller’s identity before the transaction proceeds. Agents leverage dynamic authentication measures to ensure authenticated transactions go through:
a) Knowledge-based authentication (KBA)
KBA methods are evolving to include behavioural biometrics (typing cadence, device interactions), contextual data, and voice authentication. BFSI contact centres have also invested in advanced technologies and leveraging methods like continuous learning for training agents. This multi-factor, phishing-resistant approach closes the gap exploited by AI-driven spear-phishing and synthetic-ID schemes.
b) Flagging transactions
Systems continuously flag unusual patterns, such as the rapid velocity of payments, geolocation mismatches, device inconsistencies, etc., to detect financial scams in Australia. If an anomaly is detected, high-risk transactions get routed to agents. A proactive call from the contact centre can confirm the legitimacy of the transaction before any damage is done. A combination of automated transaction monitoring and manual checks for authentication ensures that only genuine transactions proceed while intercepting suspicious transfers at source.
c) Constant communication and alerts
For transactions, contact centres trigger multi-channel notifications, including SMS warnings, email alerts, and follow-up voice calls for activities outside normal behaviour. These alerts are not only sent to customers but also to fraud teams so they can be mobilised into action. This rapid outreach not only thwarts scams at inception but also turns customers into empowered partners ready to confirm or reject questionable transactions.
d) AI and speech analytics
AI-powered speech analytics tools scan live calls for linguistic and acoustic cues, such as hesitations, stress markers, and atypical phrasing. The result of this analysis is available on dashboards with profiles and scorecards to gauge fraud risk. When anomalies surface, agents and supervisors receive instant alerts, enabling swift intervention before fraud can proceed. Modern training methodologies, such as “learning in the flow of work,” are also at play, helping agents detect subtle cues and patterns that may indicate fraud.
How TP Australia helps prevent fraud
At TP Australia, we blend global best practices with local intelligence to safeguard both BFSI organisations and their customers. We train to detect financial scams at source, equip our people, and set up processes that deflect financial losses.
Here’s what we do:
A. Tailored detection technologies: We integrate enterprise-grade AI engines trained on millions of real-world interactions with regional fraud patterns. This keeps our systems agile for detecting threats unique to the Australian region.
B. Specialised agent training: Our “Fraud Defence Academy” uses modern training methodologies to equip agents with scenario-based learning that evolves alongside emerging scam techniques.
C. Real-Time monitoring and escalation: A dedicated fraud-ops centre tracks flagged interactions 24/7, coordinating rapid escalations to minimise customer impact and financial loss.
D. Customer-first philosophy: We prioritise transparent communication, keeping end users informed without causing undue alarm and preserving trust even when fraud strikes.
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