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Enterprise Fraud Management – An Overview

Enterprise Fraud Management (EFM) is a centralized framework for risk management, providing comprehensive risk analysis and application of controls for identifying internal and external frauds across all users, accounts, and channels in the organizations by transaction monitoring and customer profiling. Furthermore, it helps in identifying malicious behaviour and corruption in real-time thereby combating risks, minimizing losses, ensuring regulatory compliance, and optimizing operational efficiencies across the organization and entities.
EFM platform gives higher visibility in identifying threats and mitigating these threats. EFM solutions also offer a unified dashboard, enabling real-time monitoring of transactions and raising alerts for anomalies if required.

Some Noteworthy Features of EFMs are –

  • Centralized Data Repository – Businesses are developing centralized data repositories for clients’ accounts and transactions data for various products and services across multiple channels. EFM solutions process large quantities of data in real-time to create detailed profiles of clients and employees using high-performance computing technology based on machine learning, which can be used to detect and investigate money laundering and fraud.
  • Fraud Risk Assessment – A fraud risk assessment is a vigorous and continuously improving process. Organizations do thorough fraud risk assessments to identify individual fraud schemes and risks, assess their probability and magnitude, check existing fraud control actions. They introduce new rules and regulations to improve fraud detection. EFM solutions use risk scores to assess fraud based on guidelines provided by enterprise firm and analysed historical information. The new cloud-based EFM solutions are flexible enough to adapt these new rules and risk assessment tasks.
  • Real-Time Detection using Analytics – Since fraudsters are becoming more advanced, EFMs must evolve at a faster rate. EFM solutions allow for in-depth analysis of internal and external data collected from all resources for real-time fraud detection. In addition to rule-based fraud detection, sophisticated predictive fraud models fuelled by analytics on massive quantities of data are being developed. Risk is assessed in real-time for each transaction using a combination of parameters, algorithms, and cumulative statistics by comparing the characteristics of each customer’s or employee’s conduct with the fraud models and recorded patterns of behaviour. Techniques like graph visualization are used to identify underlying patterns and irregularities in data. EFM’s have forensic tools for e-fraud investigation. The aim is to use all available data to detect illegal activity before it happens and to avoid it before a customer’s account is compromised.
  • Scalability and Performance – EFM solutions are cloud-based, so there are no data storage and processing limitations. Financial institutions like banks with millions of customers and billions of transactions can be monitored with EFMs while retaining the fast detection needed in real-time environments. These organizations can leverage EFM’s cross-channel fraud management, user-centric fraud detection based on advanced AI. EFM solutions can easily correlate fraud events across the organization.
  • Enterprise Case Management – Enterprise Case Management uncovers hidden relationships in financial transactions. It is created primarily for financial fraud detection and investigations in the EFM solutions, it is built on previous fraud cases. These prebuilt and streamlined cases include key areas of fraud, which ease the process of fraud detection.

In the digital era, with evolving technologies, fraud attacks are also increasing at an alarming rate, indicating organizations to include Enterprise Fraud Management solutions to mitigate threats and frauds in the risk landscape.

Anti-Money Laundering – A Recapitulation

Anti-Money Laundering Software is as straightforward as it sounds – It works against Money Laundering.
But, in efforts to understand Anti-Money Laundering, we need to understand money laundering first.

So, what is money laundering?

The term ‘Money Laundering’ originates from the innovative methods used by the Italian Mafia to channel the large amounts of money they acquired from illegal occupations, into financial institutions without raising a concern or being subjected to taxation.
For a brief explanation, ‘money laundering’ encapsulates the numerous ways in which people convert illegally obtained funds into legal money.

To prevent this from happening, many laws got put into place in the late 1900s to ensure that financial institutions (FIs) were safeguarded against illicit activities including, but not limited to, money laundering. The Financial Action Task Force (FATF) also makes sure that financial institutions also do not indulge in malpractices by implementing many protocols that FIs needed to follow.

With the addition of these security protocols and compliances, the paperwork that FIs generated suddenly grew manifold. Keeping up with all the new rules and laws was also proving difficult for bank officials. This is why Anti-Money Laundering software came into existence.

Anti-Money Laundering (AML) Software helps banks and other legal/ financial organizations collate, sort and manage customer data and transaction history to identify problematic clients and simultaneously helps them oblige to regulatory compliances.

To gain a deeper understanding of AML software, let’s look into the 4 basic types of AML software –

1) Transaction Monitoring systems – They help monitor and identify suspicious transactions based on transaction patterns and long-term user behaviour. These transactions would include abnormal behaviour, large sum transactions, and multiple consecutive transactions. These systems also put together Suspicious Activity Reports (SARs) or Suspicious Transaction Reports (STRs) that help identify long-term customer behaviour.

2) Currency Transaction Reporting systems – These specifically deal with keeping a record of large cash transactions. The sum varies from region to region. Some systems also have real-time tracking and allow FIs to verify customer identity before allowing large cash transactions.

3) Customer Identity Management systems – Customer Identity Management is an important part of KYC (Know Your Customer). It helps financial institutions identify potentially dangerous customers by checking various databases for fraudulent activity, identity theft, blacklisted persons, and other suspicious behavior. Most countries maintain an elaborate list of suspicious identities as well as PEPs (Politically Exposed Persons). These candidates are generally flagged by the customer identity management system through its name screening capabilities.

4) Compliance Management systems – As stated before, to ensure the security of FIs, many regulatory compliances have been put into place. Keeping up with these and making sure that a FI is truly compliant can be taken care of with compliance management systems. They create audit trails, keep records of proof of compliance, track employee records and training, and help handle non-compliance situations.

Some modern AML Tools utilize Artificial Intelligence (AI) to streamline the above-mentioned processes. These tools function on static and dynamic sets of rules, are capable of integrating with third-party security tools and databases, accumulating and organizing data, and can automatically screen customer profiles for potential risks.