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Financial fraud is growing and it is a costly problem , estimated at 6 % of the Global Domestic Product , more than US $ 5 trillion in 2019 .

Despite using increasingly sophisticated fraud detection tools – often tapping into AI and machine learning – businesses lose more and more money to fraudulent schemes every year . Graph data science helps turn this pattern around . By augmenting existing analytics and machine learning pipelines , a graph data science approach increases the accuracy and viability of existing fraud detection methods . The end result : Fewer fraudulent transactions and safer revenue streams .

In this white paper , we ’ ll take a closer look at how your data science and fraud investigation teams can tap into the power of graph technology for higher quality predictions in detecting first-party fraud as well as sophisticated fraud rings .

The challenge of detecting financial fraud

Stemming the wave of financial loss requires constant vigilance since fraud perpetrators continue to evolve their tactics , allowing them to evade detection .

Take for example one of the fastest-growing types of fraud in the U . S : synthetic identity theft . Fraudsters meld various false and authentic elements ( such as addresses , phone numbers , emails , employers and more ) into a synthetic identity , which they then use for fraudulent purposes . Synthetic identities pass as real identities all too frequently . Traditional fraud models that consistently flag other types of high-risk identities miss 85 % of synthetic identities according to ID Analytics .

At the same time , fraud rings – both small and large – are on the rise . With multiple parties involved in fraud , the associated loss skyrockets . In its 2018 Report to the Nations , the Association of Certified Fraud Examiners ( ACFE ) found a direct correlation between the number of participants and the cost of a fraud incident , rising from an average of US $ 74,000 for one perpetrator to US $ 339,000 for three or more perpetrators . Like pack hunters , fraudsters are a greater threat when they work together . The question becomes how to reduce losses from fraud given these challenges . �

14 www . intelligentfin . tech