Fraud Management in Banking: Detection, Prevention & More – Hitachi Solutions (2024)

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Banking fraud — is one of the most persistent issues financial institutions and their customers face and poses a serious threat to all parties involved. The Federal Trade Commission reports that it received 2.8 million fraud reports from consumers in 2021 alone, with total fraud losses amounting to $5.8 billion USD. Customers aren’t the only ones who pay for fraud: The American Banking Journal states for every dollar lost to fraud, banks see $4 in costs — and that’s without factoring in the damage fraud can do to a bank’s reputation.

As banking systems have moved online, so too has fraud, with fraudsters developing their own digitized methods of stealing customers’ identities and gaining access to their personal accounts. Under these conditions, it’s little wonder that 96% of banking customers surveyed say that security and fraud protection is either a “somewhat” or “very” important factor when choosing a bank.

To fight fraud in digital spaces — thereby securing the loyalty of existing customers and generating new business — banks must leverage innovative technology to enhance their financial fraud detection and prevention strategies.

Common Examples of Banking Fraud

One of the most frustrating things about banking fraud is that it can take many forms, with new schemes emerging daily. After all, if there’s one thing fraudsters excel at, it’s pivoting. Listed below are some of the most common forms of fraud (at present) in the banking industry:

  • Phishing: In a phishing attack, a scammer reaches out to an individual over email, text, or even a phone call posing as their banking institution. Scammers’ ultimate goal is to convince their target to click a link that loads malware, ransomware, or spyware onto their computer or to provide personally identifying information. Phishing — which is a form of social engineering — is often a gateway to other forms of banking fraud, providing criminals with a point of entry from which they can execute subsequent attacks.

    Phishing is not only incredibly common, but it’s also highly successful since scammers are often able to mimic legitimate institutions with alarming accuracy. What’s more, phishing not only poses a threat to banking customers, but also to financial institutions themselves: Bank employees are a popular target for scammers trying to gain access to internal systems, and phishing attacks are a leading cause of corporate data breaches.

  • Identity theft: Perhaps the most basic form of fraud, identity theft refers to any crime that involves someone wrongfully obtaining another person’s personally identifying information — such as their name, phone number, or address — and using it for fraudulent purposes. In many cases, criminals will use a banking customer’s stolen identity to take ownership of that customer’s online account in what is known as an account takeover attack.
  • Credential theft: Another basic form of fraud, credential theft involves stealing a banking customer’s information. However, the scope of the attack extends beyond a customer’s personally identifiable information into more confidential information, such as their ID number, password, card credentials, or Social Security number. As with identity theft, fraudsters often use these stolen credentials to stage an account takeover.
  • Wire fraud: Wire fraud broadly describes the use of telecommunications or the internet to defraud individuals, often across state or national borders. In the United States, wire fraud is a federal crime investigated by the Federal Bureau of Investigation. In the financial services sector, scammers trick banking customers into sending funds via wire transfer, often posing as a family member or friend in urgent need of financial assistance.
  • Money laundering: A form of fraud that impacts banks directly, the U.S. Department of the Treasury describes money laundering as “financial transactions in which criminals, including terrorist organizations, attempt to disguise the proceeds, sources or nature of their illicit activities.”

    In addition to funding illicit — and potentially dangerous — activities, money laundering compromises the integrity of the financial services marketplace and risks drawing banks into criminal networks. Any institution found party to money laundering — even unknowingly— could find itself subject not only to reputational damage and loss of goodwill but also legal and regulatory sanctions.

  • Application fraud: With application fraud, a scammer applies for a loan or line of credit with a bank using a stolen or synthetic identity. Once approved, the scammer will initially use the account in the expected way, making smaller purchases and scheduled repayments to create the illusion of normal account usage and gain access to new products and/or higher lines of credit. Eventually, the fraudster will make a series of large purchases with no intention of repayment before disappearing without a trace, leaving the bank on the hook for the bill. Application fraud is sometimes known as accounting fraud or account opening fraud.

While this list is a good starting point for understanding banking fraud — and fraud management in banking — it’s essential to remember that fraudsters’ methods are constantly evolving. To that end, here are some emerging forms of fraud to watch out for:

  • Fraud as a Service: A growing number of cybercriminals are offering their services up to the highest bidders in what’s known as Fraud as a Service. Other offerings in the Fraud as a Service “marketplace” include fraud training tutorials for would-be threat actors and access to specialized tools and malicious software programs.
  • Biometrics spoofing: Although implementing biometric authentication is a smart way to enhance banking fraud protection (more on that soon enough), fraudsters have already started to find ways around these security measures. With biometrics spoofing, criminals use banking customers’ photos, video clips, and even stolen fingerprints to spoof their identities for verification purposes and gain access to their accounts.

What Is Bank Fraud Detection and Prevention?

Bank fraud detection and prevention refers to the collective policies, protocols, procedures, and technologies financial institutions leverage to protect their assets, systems, and customers against fraud. Detection includes any activities related to threat monitoring, account monitoring, behavioral profiling, and proactive risk identification. On the prevention side, it includes any proactive measures related to threat mitigation, such as developing internal controls, conducting employee training, and implementing multi-layered security.

Bank Fraud Detection and Prevention Technology

To successfully combat fraud, banks must beat cybercriminals at their own game, which requires making advanced technology part of their first line of defense — technology such as:

  • Artificial intelligence: Traditionally, banks and other financial institutions have relied upon fraud monitoring solutions with rules-based engines to detect fraudulent transactions generated by risky IP addresses or multiple transactions taking place within quick succession on a new account. The problem with this approach is that the rules on which these engines operate are:
    • Hard-coded, which means they’re unable to adapt to evolving threats
    • Binary, which means they’re unable to accommodate the complexity of a wide variety of input variables and are prone to false positives
    • Unable to detect fraud in real-time, only flagging fraudulent transactions after a financial loss occurs
  • Artificial intelligence (AI) is the key to overcoming these challenges. AI-based fraud monitoring systems can ingest and parse massive quantities of data — a must, given the high volume of transactions banks process each day — and detect fraudulent activity in real-time.
  • Compared to rules-based engines, AI is highly adaptable, enabling banks to easily pivot their fraud management strategy based on new and emerging threats. And finally, AI offers far greater accuracy than manual or rules-based fraud detection, significantly reducing the rate of false positives and providing banking customers with a better overall experience.
  • Machine learning: Machine learning, a subset of AI, is a powerful tool for fraud prevention in the banking industry. Machine learning enables fraud monitoring and detection systems to “learn” from behavioral data, consortium data, and other internal and external data sources and adapt accordingly. The result is that banks are better able to navigate the increasingly complex fraud landscape and deliver more proactive protection to their customers and their assets.
  • Biometric authentication: Biometric authentication is an identification technique that relies on a customer’s unique physical characteristics, such as their voice, facial features, or fingerprints, to verify their identity. Each of these characteristics is known as biometric data.

    Biometric authentication has quickly become a popular security measure with financial institutions because customers’ biometric data cannot be stolen, forgotten, or lost. Although fraudsters can spoof a customer’s biometric data, it is far more challenging to do so than it is to steal their identity or credentials. To get the greatest value out of biometric authentication, banks should pair it with other technologies and controls to create a truly multi-layered security strategy.

  • Two-factor and/or multi-factor authentication: Two-factor (2FA) and multi-factor authentication (MFA) are identification techniques that require banking customers to provide two or more pieces of evidence to verify their identity. 2FA and MFA are fairly standard security measures that, like biometric authentication, should be layered with the other technologies shown here to create a comprehensive anti-fraud strategy.
  • Advanced analytics: Financial institutions process hundreds — even thousands — of transactions each day, each of which generates data. When analyzed using advanced data science techniques, customer and transaction data can be incredibly potent, enabling banks to gain a 360-degree view across the business, enhance operational efficiency, and engage in predictive fraud detection.

6 Strategies for Fighting Fraud in Banking

In addition to investing in the proper tools and technology, financial institutions can take the following measures to support bank fraud detection and prevention:

  • Host regular fraud awareness training. Bank employees are a popular target for cybercriminals — particularly for phishing attacks and other forms of social engineering — so it’s essential to educate staff about recognizing potential fraud and what to do if they suspect that they’ve been compromised.
  • Be on the lookout for internal fraud. With banking fraud, sometimes the call comes from inside the house. Employees can expose their employers to substantial risk, whether accidentally — for example, unknowingly clicking a phishing link or using an easy-to-guess password — or intentionally.

    Fraud awareness training can go a long way toward preventing accidental fraud, but institutions need to be on high alert for intentional fraud and keep a close watch on employees who:

    • Have access to accounts without a legitimate business purpose
    • Frequently access or monitor a customer’s account without a legitimate business purpose
    • Process transactions outside of work hours
    • Have unusual transfers on their general ledger
    • Transfer funds from customer accounts to their personal accounts
    • Make excessive transfers or cash deposits
  • Create a database of known threats. It’s crucial that banks be aware of and on the lookout for active and emerging threats. By collecting fraud data from internal and external sources, banks can gain a comprehensive view of the fraud landscape and make more informed risk decisions. Financial institutions can also use such a database to support fraud awareness training and enable employees to recognize a broader range of potential threats.
  • Educate banking customers. Customer fraud awareness is every bit as important as employee fraud awareness and can help a bank’s customers protect themselves against would-be threat actors. Adding educational resources to an existing knowledge base, similar to what Wells Fargo has done with its Fraud Education Library, can be an effective means of keeping customers in the know.

    And customer education shouldn’t end at fraud: Banks should also make customers aware of advanced security measures, such as 2FA or MFA and biometrics authentication, to increase their likelihood of opting in.

  • Monitor transactions in real time. Transaction monitoring is not only essential to comply with Know Your Customer standards and Anti-Money Laundering laws, it’s also an effective way to detect fraudulent activity.

    For transaction monitoring to be successful, banks must first develop behavioral profiles that establish a baseline for each customer’s normal activity. Once an institution has created a behavioral profile for a customer, it can monitor that customer’s transactions against the baseline and proactively flag any anomalous (and potentially fraudulent) activity.

  • Develop multi-layered security systems. A successful bank fraud prevention and detection strategy should be multi-faceted and include administrative, physical, and technical controls:
    • At the administrative level, institutions should develop policies, procedures, and guidelines that reduce their exposure to risk, such as security education and awareness programs and password management policies.
    • At the physical level, institutions should instate tangible security measures, such as restricting access to specific computer programs and data files and cross-checking asset or liability values against those documented in control records.
    • At the technical level, institutions should implement technology that will reduce their risk exposure, such as firewalls, anti-virus and anti-malware software, and AI-powered fraud monitoring systems.

Banking Fraud Protection in Action

To get a better sense of what a bank fraud detection and prevention strategy might look like, look no further than these two recent projects Hitachi Solutions worked on:

Customer Success Story #1

The CFO team of a major North American bank partnered with an internal audit team and Hitachi Solutions to develop a bank fraud detection, prediction, and prevention platform. The primary objective of this project was to develop multiple specialized solutions in the following areas:

  • Corruption
  • Cash fraud
  • Billing fraud
  • Check tampering fraud
  • Skimming
  • Financial statements fraud
  • Internal fraud/cheque kiting

We leveraged our deep business expertise and profound knowledge of the datasets required to successfully enable each of these solutions with the following capabilities:

  • Sophisticated visualizations that allow the customers to identify anomalous patterns within its data and make timely decisions
  • Catalogs of curated assets with secure enterprise catalog management supported by a data governance framework
  • Fully scalable machine learning models (with a full range of integrated tools required to manage, retrain, and reevaluate those models)

In deploying this platform, the customer has realized the following business and financial benefits:

  • Compliance with key industry regulations
  • Proactive risk management and mitigation
  • Reduction in financial losses
  • Greater control over internal and external processes
  • Reduced risk to their customers
  • Stronger customer relationships

Customer Success Story #2

The fraud team of a credit union approached Hitachi Solutions in the interest of implementing a bot to evaluate suspicious mobile deposits as a fraud prevention measure. The customer originally had automated alerts to validate mobile deposits over $2,000 in value, allowing deposits under this threshold to process without review and leaving the customer vulnerable to fraud risk.

The new mobile alert deposit bot we built for the customer will decrease losses incurred due to fraudulent mobile deposits, allowing the customer to realize savings of more than $50,000 in loss prevention. Other predicted outcomes for this project include:

  • Increased productivity through the elimination of manual processes
  • Reduced risk of potential fraud and returned checks
  • Broadened scope for evaluating deposits
  • Elimination of duplicate work between the customer’s fraud and item processing teams
  • Greater control and savings realized related to the “holds” process

For this project, Hitachi Solutions evaluated various approaches to intelligently automate the validation of deposit data with Power Automate, Microsoft’s low-code automation platform. Additionally, we leveraged AI Builder, a Power Platform capability that provides AI models designed to optimize business processes. With AI Builder, we used a standard AI model for document and invoice processing to automate the customer’s validation process.

As you can see from these examples, Hitachi Solutions provides low-code solutions, pro-code data, data science, and AI solutions to support banks with financial fraud detection and prevention. Combat fraud by taking total control of your data — contact us today to get started.

Fraud Management in Banking: Detection, Prevention & More – Hitachi Solutions (1)

The Who & What Behind the Blog

Landy serves as Industry Vice President for Banking and Capital Markets for Hitachi Solutions, a global business application and technology consultancy. He joined Hitachi Solutions following the acquisition of Customer Effective and has been with the organization since 2005.

Fraud Management in Banking: Detection, Prevention & More – Hitachi Solutions (2024)

FAQs

What is AI solution for fraud detection? ›

AI fraud detection is an artificial intelligence use case that employs algorithms to analyze large datasets containing information about transactions. These fraud detection algorithms are designed to recognize patterns and identify discrepancies that indicate fraudulent activity.

What are the key processes used for fraud detection? ›

Data mining: Data mining is a process of extracting patterns from data. It can be used to detect fraud by finding unusual patterns in data that may be indicative of fraud. The patterns found in data mining can be used to create predictive models that can be used for future detection, too.

How do I stop a scammer from using my bank account? ›

Log in to Online Banking to view your Security Meter level.
  1. Keep your contact information up to date. ...
  2. Create the strongest possible passwords. ...
  3. Allow push alerts on the Mobile Banking app. ...
  4. Protect your devices. ...
  5. Enable biometrics (fingerprint sign-on or facial recognition) ...
  6. Know the red flags that signal a scam.

Which is the most effective way to prevent and detect fraud? ›

Protect checks against fraudulent use.

Deface and retain voided checks. Store blank checks in a locked drawer or cabinet, and limit access to the checks. Require that checks are to be signed only when all required information is entered on them and the documents to support them (invoices, approval) are attached.

How to trick AI detection? ›

Techniques for How to Avoid AI Detection
  1. Using Unicode Characters. ...
  2. Adding Punctuation and Symbols. ...
  3. Using hom*oglyphs. ...
  4. Using Synonyms and Antonyms. ...
  5. Rearranging Sentence Syntax. ...
  6. Changing Word Forms. ...
  7. Visual Camouflage Techniques. ...
  8. Audio Encoding Methods.
Jul 10, 2024

How do banks use AI to detect fraud? ›

AI prevents fraud by analyzing data in real-time to detect unusual patterns, using machine learning to adapt to new fraud tactics. It automates detection, reduces manual review needs, and enhances response speed to threats, thereby effectively safeguarding assets and customer trust.

Which database is used for fraud detection? ›

Graph databases are particularly adept at uncovering complex fraudulent networks that involve multiple actors collaborating to commit fraud. The system can traverse the graph to reveal hidden relationships and connections between seemingly unrelated entities, exposing the intricate web of fraudulent activities.

What data is needed for fraud detection? ›

Machine Learning and AI

The models use transaction data to analyze patterns and anomalies to detect unusual activities, and user behavior data to identify deviations from typical usage patterns that enable it to flag potentially fraudulent activities.

Can someone access your bank account with routing and account number? ›

If a fraudster knows your routing number they can easily tell which financial institution your funds are at, putting you at risk of phishing attempts. If a criminal has both your routing number and account number they can potentially steal money from your account through fraudulent ACH transfers and payments.

Can a scammer access my bank account with my phone number? ›

A phone number alone is not enough for someone to hack your bank account. But, as outlined above with regard to someone having your email address, someone with your phone number could potentially call or text you as part of a phishing scam with the ultimate aim of hacking money from your accounts.

Can I block someone from taking money from my bank account? ›

Call and write your bank or credit union

Tell your bank that you have “revoked authorization” for the company to take automatic payments from your account. You can use this sample letter . Some banks and credit unions may offer you an online form.

What is the no 1 way to detect fraud? ›

An anonymous tip line (or website or hotline) is one of the most effective ways to detect fraud in organizations.

What anti-fraud controls are the most common? ›

Internal controls and fraud prevention: The top four internal controls in accounting
  • Internal and external audits. ...
  • Segregation of duties. ...
  • Anti-fraud policies and ethical code of conduct. ...
  • Accurate and up-to-date financial reporting.
Nov 1, 2023

What is one method that help control online fraud? ›

Do not share passwords and do not leave any documents that contain access to financial data in an unsecured area. Change your passwords regularly for better protection, using a combination of letters, numbers and special characters when possible.

How is AI used for lie detection? ›

A desktop app called LiarLiar purportedly uses AI to analyze facial movements, blood flow, and voice intonation in order to detect deception. Its founder, a Bulgarian engineer named Asen Levov, says he built the software in three weeks and released it last August.

How artificial intelligence can fight fraud? ›

AI fraud detection uses machine learning algorithms to analyze behaviors and detect anomalies that indicate fraud. These systems start with a baseline of normal transaction patterns and user behaviors and then continuously monitor data to look for deviations from the norm.

What is generative AI for fraud prevention? ›

Using Generative AI for fraud detection and prevention helps analyze patterns in data and identify potential risk factors. This approach enables companies to spot early indicators of possible fraudulent behavior and speed up the development and testing of new detection models​​.

How can we use AI to detect crime? ›

Predictive analytics and machine learning

One way that AI is integrated into RTCCs is by using past data to estimate probable criminal activities or locations. AI models can find patterns and trends in crime data and generate predictions based on various factors, such as time, location, weather and events.

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