Intercept Contraband and Assure Revenues
Identification of fraudulent parcels is a growing concern for delivery organizations and border customs agencies. Parcels and mail are used to ship drugs and firearms. They can contain counterfeit products, which devalue intellectual property and skirt taxes. Fraudulent mail results in underpayment of customs and postage fees. The impacts of fraudulent parcels to the health of society, to taxation, and to revenue cannot be understated.
Field experts can use intuition and experience to intercept such contraband. However, using field operatives for fraud interdiction is unsustainable due to human error, temptation, and the sheer scale of most customs and shipping environments.
Businesses and organizations must develop and implement risk-based predictive models using their own empirical examples to predict the likelihood that mail is fraudulent or contains contraband. A data-driven model will now be required to handle the high (and growing) volume of ecommerce mailings. The journey also includes using more relevant data sources to increase the risk-based scores and improve field performance.
Using machine learning, risk-based, predictive identification platforms will save millions of dollars in the mailing and parcel supply chain, cut operational costs, and stop crime across state and international borders.
In 2017 alone, over 72,000 Americans died of drug overdose, with much of the volume in illicit drugs coming from across borders. We can stop this serious impact to our families and society as a whole while securing revenues.
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Scalable, Automated Fraud Detection in a Postal Environment