A retail scenario that one would dare to confront – A product is whisked away without being scanned at the counter; a merchandise is given without being charged; and a mal-intentioned individual goes scot free after returning a product (no rhyme or reason to return the product) and discounts are being given to transactions that aren’t worthy of discounts at all – possible scenarios among the many ill-intentioned fraudulent activities that put a robust Retail loss prevention program in perspective.
Reducing retail shrink is now a top priority for retailers to consolidate their profits, cut losses by using AI/Machine learning to transform retail loss prevention. As the retail sector grapples with shrink and loss that comes with it, perpetuated using shoplifting, internal theft, return fraud, vendor fraud, discount abuse and administrative errors among others – data, and specifically patterns, behavioral insights and correlations sensed from data, is forewarning (predict) them of any possible fraudulent activity and forearming them with requisite measures to protect retail shrink and loss.
Reactive to proactive loss prevention strategy
There is a profound change in framing the loss prevention programs – Going from reactive to proactive ways of predicting and preventing retail shrink and loss. It begins with capturing data from critical sources including security systems (CCTV, camera, access control and alarm records), video, store operation applications, POS, payment data, crime data (local crime statistics), store profile, supply chain data, employee data, customer data, ORC data map and e-commerce platforms.
The data serves as a critical feed to leverage techniques such as behavioral analytics, predictive analytics and prescriptive analytics, computer vision, deep learning, image processing & recognition, machine learning and correlation, and pattern recognition.
In using AI/Machine learning to transform Retail Loss prevention, proactive steps help arrest retail loss, augment KPIs to prevent inventory loss, shoplifting, theft, pilferage, discount abuse and return fraud and reduce shrinkage. It also helps make the transition from ‘Identifying a case’ to ‘Preventing a case’.
AI/Machine learning to transform retail loss prevention – A case in point
Here is a case in point – using machine learning to prevent return fraud.
Return behaviors could be the cue to prevent fraudulent returns. Return authorization is like walking on eggshells; striking a balance between great customer experiences and making sure that it is not a fraudulent move can be the feared tight-rope walk. The shopper, purchasing behavior, return behavior and store trends make up for a lively feed for the machine learning algorithm to predict and prevent return fraud.
Reading the Risk tea-leaf for a comprehensive loss prevention program
Reading the risk tea-leaf is another advanced analytics way to predict and prevent retail loss. By keeping tab on retail loss risk factors, retailers can leverage machine leaning algorithms and data science techniques to identify leading loss factors, predict and prevent loss. Some nagging queries disturbing retailers, in terms of loss prevention strategy, such as
- Is this store prone to theft and loss?
- Does this product lead to retail losses?
- Is this transaction devoid of suspicions?
Leveraging advanced analytics, it will be worthy to predict ‘risky’ areas in terms of high-risk products, high-risk transactions, high-risk stores and high-risk relationships. Keeping risk-ranking predictive models built leveraging statistical modeling as well as machine learning at the forefront help ward off fraudulent activities resulting in retail loss.
Combating retail shrink with the right loss prevention strategy
Shrink is a perennial retail pest. Combating retail shrink begins with a robust loss prevention strategy. Taking stock of the existing loss prevention technologies used to augment KPIs, retailers strike gold by expanding the LP spectrum to include mobile, online and other customer engagement channels and use AI/Machine learning to transform retail loss prevention.