House Financial Services Committee Report Says Artificial Intelligence Can Both Enable Scams and Strengthen Fraud Detection
Republican members of the U.S. House Committee on Financial Services have called for broader adoption of artificial intelligence-powered fraud detection systems as criminals increasingly use advanced technology to carry out financial scams.
The recommendations follow a year-long examination of financial fraud and related hearings held by the committee. Lawmakers argue that banks, regulators and law enforcement agencies need modern analytical tools to respond to increasingly sophisticated criminal networks.
The committee has also advanced legislation intended to examine how artificial intelligence and other advanced technologies can improve fraud prevention, particularly for smaller financial institutions.
AI Is Transforming the Fraud Landscape
Artificial intelligence has emerged as a significant risk in financial crime because it can help fraudsters produce highly convincing and personalised communications at scale.
Criminals may use generative AI to create:
- Realistic phishing emails and messages
- Fake business correspondence
- Voice clones of relatives or company executives
- Deepfake videos
- Fabricated identification documents
- Automated social-engineering campaigns
Such tools can make fraudulent communications appear more authentic and make it harder for victims and conventional security systems to distinguish genuine activity from deception.
Deepfakes Could Be Used to Authorise Payments
One growing concern involves the use of AI-generated audio and video to impersonate senior executives, family members or trusted officials.
In a corporate setting, a convincing deepfake could be used to instruct an employee to approve a transfer, disclose confidential information or alter payment details.
These risks increase the need for secondary verification procedures, such as independently confirming high-value transactions through a separate communication channel.
Financial Institutions Need Comparable Technology
Committee Chairman French Hill has argued that fraud prevention capabilities must evolve alongside criminal methods.
The committee’s position is that authorities cannot effectively address technology-enabled financial crime using outdated detection systems. Financial institutions, regulators and enforcement agencies require tools capable of analysing transactions, identities and behavioural patterns in real time.
U.S. Consumers Reported $15.9 Billion in Fraud Losses
The scale of the problem is reflected in data from the U.S. Federal Trade Commission.
In 2025, the FTC received approximately 3 million consumer fraud reports, with reported losses totalling $15.9 billion. This was substantially higher than the more than $12 billion in losses reported for 2024.
The figures represent reported losses and may not capture every fraud incident, as many victims do not report scams to authorities.
AI Can Also Strengthen Fraud Prevention
Although artificial intelligence creates new risks, it can also help institutions identify suspicious conduct earlier.
AI-powered systems can analyse large volumes of transactions and flag unusual activity based on factors such as:
- Sudden changes in payment behaviour
- Transactions inconsistent with a customer’s history
- Unusual device or location activity
- Rapid movement of money through connected accounts
- Multiple accounts linked to common identifiers
- Suspicious login or authentication attempts
Unlike fixed rule-based systems, machine-learning models can identify complex patterns that may not be immediately visible to human investigators.
Real-Time Monitoring Could Reduce Losses
AI-based fraud detection tools can assign risk scores to transactions before they are completed.
A high-risk payment could be temporarily delayed, subjected to additional verification or referred to a fraud investigator.
However, such systems require careful supervision because inaccurate models may generate excessive false alerts or unfairly restrict legitimate customers.
Human oversight remains essential when automated systems influence significant financial decisions.
Proposed Law Targets Fraud-Detection Technology
The House Financial Services Committee has advanced the Bank Fraud Technology Advancement Act of 2026.
The proposal would direct federal financial regulators to assess how banks and other institutions are using AI and advanced technologies to combat fraud. It would also examine barriers preventing community banks and credit unions from accessing such systems and establish a voluntary technology pilot programme.
The committee approved the measure by a recorded vote of 52–1.
AI PLAN Act Seeks Coordinated Government Strategy
The committee also advanced the Artificial Intelligence Practices, Logistics, Actions and Necessities Act, known as the AI PLAN Act.
The measure seeks a coordinated federal approach to AI-related risks and would involve agencies including the Departments of Homeland Security, Commerce and the Treasury.
The committee approved the proposal by a recorded vote of 52–0, although further legislative action would be required before it could become law.
Responsible Deployment and Oversight Remain Essential
Wider use of artificial intelligence will not eliminate financial fraud on its own.
Institutions deploying AI-based monitoring systems must also address:
- Data accuracy and quality
- Customer privacy
- Cybersecurity
- Algorithmic bias
- Explainability of automated decisions
- False-positive alerts
- Human review and accountability
Fraud-detection models should be regularly tested to determine whether they remain effective as criminal methods and customer behaviour change.
AI Is a Double-Edged Tool
Former IPS officer and cybercrime expert Prof. Triveni Singh described artificial intelligence as a double-edged tool in the fight against financial crime.
He said criminals are increasingly using deepfakes, voice cloning and automated social engineering, while banks and investigators are deploying behavioural analytics, transaction monitoring and digital forensic tools to detect suspicious activity earlier.
According to him, responsible AI adoption must be supported by strong cybersecurity systems and close coordination between financial institutions and government agencies.
Human Verification Still Matters
Even advanced detection systems may fail when criminals successfully manipulate legitimate customers or employees into authorising transactions themselves.
Financial institutions should therefore combine AI monitoring with:
- Multi-factor authentication
- Independent transaction confirmation
- Employee fraud-awareness training
- Customer alerts
- Restrictions on unusual high-value transfers
- Rapid reporting and account-freezing procedures
The most effective anti-fraud strategy is likely to combine technology, human judgement, strong internal controls and cooperation between institutions.