BPM Analytics

Detecting and preventing banking fraud

As digitisation improves the banking experience for customers, it also invites fraudsters to use technology to cheat businesses and customers in novel ways. The recent ACFE survey reported that organisations lose at least 5% of their annual revenue to fraud each year. Banking fraud spiked 159% in the USA in 2021 compared with 2020. Also, 93% of banking frauds occurred online, according to the latest Financial Crime report. Thankfully, multi-layered banking frauds are preventable using modern technology, awareness, and proactive approaches to fraud prevention and risk management.

Most common banking frauds

Sophisticated and ever-changing frauds require banks to deploy fraud management solutions that offer better defence against various types of fraud in the sector.

  • Wire fraud involves using various communication modes to convince the bank that large sums of money have been transferred from legitimate sources while covering up with a fake identity.
  • Identity theft or credential stealing is often carried out through emails or SMSes, commonly called phishing. Fraudsters dig for sensitive information from legitimate customers by posing as government authorities or banks.
  • Account takeover is a worse form of identity theft where fraudsters use sensitive customer data to completely take over the account for siphoning funds and carrying out transfers.
  • Money laundering involves ‘cleaning’ the illegally obtained money using the banking route by carrying out a bank transfer from one account to another.
  • Accounting fraud is enabled by fraudsters by setting up accounts with banks to apply for loans using false statements of ghost businesses. On securing the funds, the fraudsters disappear, leaving the bank to deal with the loss.

How can the banking sector prevent fraud?

A proactive approach to fraud detection and prevention is imperative to ensure customer trust, employee compliance, and overall improvement in operational efficiency.

  • Monitor transactions:

    Transaction monitoring is required in some cases of crimes. Monitoring regular customers’ web and app activity can nip the nuisance in the bud.
  • Check internal threats:

    Banks should address internal threats by educating employees and providing them with a confidential hotline to provide tips on fraudulent activities. They can mitigate internal threats by deploying workforce analytics and behaviour profiling to sift through large data of inactive accounts. Regular internal audits and employee profiling at the HR level are other ways to minimise the risk of internal threats.
  • Educate customers:

    Educating customers can reduce fraudulent activities such as phishing and identity theft. Businesses should educate their customers about safe transaction tips to reduce frauds.
  • Use real-time data:

    Enrich your database with real-time data from other sources, digital services, and social networks. This creates a more comprehensive customer profile for predictive analytics. Real-time email domain verification, IP address checks, and device recognition methods can alert customers in case of suspicious activities.
  • Deploy machine learning:

    Machine learning (ML) algorithms are at the heart of automated fraud analytics, which use large amounts of data to predict behaviour patterns, content anomalies, bot clicks, and other suspicious activities. ML can also detect loopholes in cybersecurity, check for system weaknesses, and help fix them.

Fighting frauds with tech

We are aware that the identifiable classifications of fraud do not exist exclusively. While many tools are already at work detecting and preventing fraud, about 42% of complex fraud is exposed by whistleblowers informing businesses or authorities. More advanced methods based on technology can empower the banking sector.

  • Biometrics:

    Biometrics is the answer to verification issues and preventing credential theft. It adds another layer of protection for account security as biometric data is hard to crack and duplicate. Certain biometric techniques such as fingerprint and facial recognition software are already widely used. Voice cadence also carries a unique signature and is used by banks to add an additional security layer to prevent fraud.
  • Artificial intelligence:

    Manual verification of a large volume of transactions is an error-prone and time-consuming activity. Artificial intelligence uses RPA to monitor transaction checks and flag suspicious activity.
  • Data integration:

    Consortium data within the sector provides collective intelligence on fraudulent activities. Moreover, data integration can avoid silos within the organisation and create a clear picture of customer profiles and transactions to flag risky activity.

For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

How can Infosys BPM help?

The industry insights of Infosys BPM can help fraud management in finance by deploying our wide range of fraud detection and prevention solutions using AI/ML and predictive analytics to drive business outcomes. We work with businesses to analyse large data, reduce fraud by detecting anomalies, and provide advanced fraud risk management in banks.

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