Financial Fraud Analytics

  • Post last modified:19 May 2023
  • Reading time:9 mins read
  • Post category:Business Analytics
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What is Financial Fraud Analytics?

Financial fraud analytics is the practice of using data analysis techniques to identify and prevent fraudulent activity in the financial industry. It involves using statistical methods, machine learning algorithms, and data visualization tools to analyze large volumes of data and detect patterns and anomalies that could indicate fraudulent behavior.

Financial analytics is a part of BI & EPM with an impact on every arena of a business. It plays a vital part in business profit calculation helping you respond to all sorts of questions in regards to your business while allowing you to forecast your business’s future.

Modern-day businesses need prompt information to make decisions and every organisation requires strong financial forecasting and planning. The various requirements of the traditional financial department and technological growth need analytics of its finance, therefore; financial analytics help in shaping the future goals of a business and can help in improving its strategies to make decisions.

It helps you to focus on calculating and operating tangible assets like cash and equipment which provides detailed insight into the financial status of the business and improves the profitability, cash flow and value of a business.

Here are certain critical financial analytics which an organisation needs to implement irrespective of size:

  • Predictive sales analytics: An informed forecast of sales that helps to plan and manage highs and lows

  • Client profitability analytics: Helps in analysing client groups and receiving useful insights ‰‰ Product profitability analytics: Helps establish profitability insights throughout the range of products assisting to better decide and protect profit and growth

  • Cash flow analytics: Predicting cash flow through tools like regression analysis and assisting with cash flow management to ensure having enough money for daily operations

  • Value-driven analytics: Assesses business value drivers ensuring they deliver the expected outcome

  • Shareholder value analytics: Measures the financial impact from a strategy and reports how much value that strategy is generating to the shareholders and is utilised concurrently along with profit and revenue analytics through tools like Economic Value Added (EVA)

What is Fraud Analytics?

Fraud analytics is the use of data analysis techniques to detect and prevent fraudulent activities. It involves the application of statistical and machine learning algorithms to large datasets, in order to identify patterns and anomalies that may indicate the presence of fraudulent behavior.

Fraud analytics on the other hand is utilisation of big data analysis techniques to keep away online financial fraud. It helps financial organisations to predict behaviours towards future fraudulent activities while helping them apply prompt detection and easing of fraudulent activity in no time.

Online banking is getting used by more people or they are managing their finances online every year. The global lockdown owing to COVID19 in 2020 has convinced even more customers towards using online banking for at least a certain part of their financial conduct.

Online fraud, which is already increasing every year has followed suit too. Account takeover (ATO) which is a peculiar and popular type of financial fraud has multiplied 280 percent just between Q2 of 2019 and Q2 of 2020, therefore; financial institutions must apply detailed measures of fraud management immediately to protect the accounts of their customers.

Online fraud has been evolving consistently and with banks putting remediation measures, new threats are appearing regularly and the traditional static rules-based prevention system is not able to keep tempo.

As a relief, there is an availability of a wealth of data to financial organisations which can get used in predicting and detecting financial fraud and get ready for new threats. Username and password collection at login are not sufficient anymore to guard against fraud.

With someone accessing or attempting to access an account, other types of data can get used to realise the legitimacy of a customer which helps to determine the legitimacy of the requested transaction. Such data consists of:

  • Type of device being used
  • Whether that device is previously registered
  • Whether fingerprint is available to verify identity
  • Whether the requested transaction fits into their historical patterns

Fraud impacts organisations in several ways which might be related to financial, operational or psychological processes. While the money-related misfortune owing to fraud is huge, the full effect of fraud on an organisation can be more shocking. As fraud can be executed by any worker inside an organisation or by an external source, an organisation needs to have successful fraud management or a fraud analytics program to defend its reputation against fraud and prevent financial loss.

Numerous organisations stay helpless against extortion and money-related crime since they are not exploiting new abilities to battle today’s dangers. These abilities depend intensely on huge information and analytic innovations that are currently accessible. With these advancements, organisations can oversee and examine terabytes of recorded and outsider information. The capacity to break down enormous information volumes empowers organisations to make exact models for perceiving and forestalling future fraud.

By utilising the most recent advancements in robust analytics, organisations can unhesitatingly ensure themselves and their clients regarding privacy and security of data while doing business with them or offering them various services which require their data to be utilised. Advanced analytics can also be connected to all key fraud information to foresee whether an activity is possibly fraudulent before losses happen. Taking a look at just little arrangements of security information, for example, occasion logs decrease a bank’s capacity to anticipate or identify sophisticated crime.

Intelligent investigation of suspicious movement requires performing and managing requests that are bolstered by careful investigation and data availability. With these tools, organisations can rapidly confirm fraud and then further activities, such as prosecution and recuperation can be taken.

Organisations can use the already recorded information and analyse it to detect and prevent fraud in the future. This information also helps in detecting the past and future impressions of the fraud. The recorded information related to fraud can help organisations to prevent huge losses of money and data related to it or clients.

Data management software empowers auditors and fraud analysts to break down an organisation’s business information to gain knowledge into how well internal controls are working and distinguish transactions that appear to be fraudulent. Generally, data analysis can be done at places in an organisation where electronic transactions are recorded and stored.

The companies also use whistleblower hotlines which help individuals for reporting speculated fake conduct or unsafe conduct and violations of its law and policy. However, using hotlines alone is insufficient.

Why be just receptive and wait for a whistleblower to come forward at the last approach? Why not search out indicators of fraud in the information?

To successfully test for fraud, every important transaction must be analysed overall pertinent business frameworks and applications. Breaking down business exchanges at the source level provides auditors with better knowledge and a more entire view with regards to the probability of fraud happening.

Analysis involves the investigation of those activities that are suspicious and help control weaknesses that could be misused by fraudsters.

Article Source
  • Baesens, B. Veronique. Wouter. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. Wiley.

  • Wikimedia Foundation. (2021, November 22). Health Care Analytics. Wikipedia. Retrieved January 25, 2023, from

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