Artificial Intelligence

Artificial Intelligence Takes On Transactional Fraud

In the field of fraud prediction, with transactional fraud raising every day; investors, board, management and business heads are keen to look at possibilities of detecting fraudulent transactions before they pass through the system. Machine learning algorithms bring efficiency in identifying potentially fraudulent transactions. Certain factors require critical consideration while adopting machine learning for fraud prediction. Through this article find out how these factors are vital in making an effort towards creating machine learning driven fraud prediction meaningful.

Artificial intelligence has evolved into mainstream businesses over the past few years. With the sheer computing capability and the ability to identify patterns that are not always recognizable by humans the choice of adopting AI is inevitable. In the field of fraud prediction, with transactional fraud raising every day, investors, board members, management, and business heads are keen to look at possibilities of detecting fraudulent transactions before they pass through the system.

The prediction of fraudulent transactions evolved from rule-based exceptions, which developed into a more mature machine learning that is a more driven prediction and part of the mainstream today. There is an increased interest and attraction towards the adoption of machine learning driven fraud prediction models. These models built with ensemble algorithms, bring efficiency in identifying potentially fraudulent transactions and also helps the investigator to have sufficient insights about such transactions before deciding on the same.

Amidst the interest, excitement, and opportunities in adopting machine learning for fraud prediction, certain factors require critical consideration. These factors are vital in making an effort towards creating machine learning driven fraud prediction meaningful.

  1. Definition and determinants of frauds: In a business environment, frauds could be of multiple types and classifications. Not all of these classifications have determinant driven by data as some of the frauds have determinants in nature of physical inspections, hard copy documentation review and interviews/discussions. Hence its key to determining the fraud that we would like to predict using machine learning and also reflect the determinants or data variables that are key for such prediction.
  2. Defining fraud actors and related triggers: The category of fraud perpetrators could be different for different types of frauds. For example, in a loan application with falsification fraud, the perpetrator could be the customer or a direct selling agent who had submitted the application through falsified documentation to achieve targets. Defining the fraud actors relevant to defined fraud is essential. Also, an identifier for fraud actors along with relevant transactional triggers shall also be listed out to help in using these insights appropriately. The model predictions shall classify the transactions based on fraud actors.
  3. Data, Data, Data: Machine learning algorithms require data. The data shall be relevant, qualitative, adequate and unbiased. Not all data that you may have is relevant, qualitative, or adequate. Qualitative data reflects on the completeness and correctness of the data considered for the machine learning. Bias in the source data may make the predictions inconsistent thereby impacting the results, specifically when it would question the integrity of a customer/employee as the case may be. In light of the fact that certain predictions may be difficult to validate, having an unbiased data set is of utmost importance.
  4. Validation mechanism for predicted outcomes: It is hypercritical to understand that fraud prediction models with limited or no human intervention may not be possible in all circumstances. Instead, it is critical also to know how humans along with machine learning intelligence can better prevent frauds. A structured validation mechanism of the transactional prediction is essential to make a machine learning model successful. The validation mechanism shall be focused on using the predicted outcomes to gather pieces of evidence that proves or disproves the prediction. These outcomes could be essential feed for enhancing the model accuracy and precision.
  5. Monitoring prediction outcomes: Black box approaches for machine learning are prominent, but they do not necessarily exhibit the determinants for prediction. While alternative researches and approaches towards understanding the predictions or patterns learned are in progress, more attention is required towards monitoring the outcomes. Monitoring the input data trends and changes in the patterns over the period and cross-correlating the same with the changes in the prediction, running mock transactions and review of false positives to identify the extent to which the model needs to be tuned is critical. Monitoring is a critical role that is required to avoid possibilities of over-reliance on prediction systems.

While these factors cannot be considered as exhaustive, these are essential factors for an effective outcome in AI-driven fraud prediction efforts. Technology is a great aid in the direction we drive them. A responsible model building can bring more holistic impact through solutions that we build for fraud prediction using Machine learning.

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Sundar Narayanan

Sundar NarayananSundar Narayanan is a fraud examiner by qualification and profession. He currently leads the Forensic Services division in SKP Group in India. He has extensive experience in employee fraud, senior management fraud and anti-corruption compliance reviews. Sundar has made several presentations and conducted training for clients on fraud investigation and anti-corruption compliance, including presenting two papers at the Wharton School on anti-corruption.  He frequently writes on anti-bribery and corruption and investigation techniques.

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