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Preventing Fraud and Boosting eCommerce with Data Science

November 2021

Slides

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The eCommerce industry is growing rapidly year by year. According to eMarketer, in 2020 alone, the year that Covid-19 came into our lives, retail eCommerce sales reached $4.28B. As an industry expanding so fast, it has many challenges, such as high customer expectations, new payment and shipping flows, including omnichannel sales, and more. As with any highly profitable industry, it also attracts many fraudsters, making fraud and payment declines one of the significant causes for lost potential eCommerce revenue.

In this session, we'll discuss those challenges and share how data science, by implementing various supervised and unsupervised models and sophisticated feature engineering based on deep domain expertise, helps us prevent fraud and get the right consumers safely and successfully to the checkout.

Three takeaways:

  • eCommerce is driving a retail boom and customers expect an amazing experience

  • There are a plethora of data science use cases across the eCommerce value chain

  • Data science plays a key role in enabling eCommerce to combat fraud and prevent loss of revenue for merchants

Summary

The conversation focused on the increasing role of data science in e-commerce, illustrating its application in areas such as fraud prevention, A/B testing, recommender systems, and pricing optimization. As e-commerce continues to grow worldwide, there's a rising need for smooth customer experiences and effective fraud prevention solutions. Elad Cohen, VP of Data Science at Riskified, detailed how AI and machine learning are key in securing online transactions and enhancing revenue through data-driven insights. The discussion also touched on the difficulties of implementing data science solutions, stressing the significance of continuous learning, feature engineering, and the decision between creating or purchasing fraud prevention systems.

Key Takeaways:

  • Data science is essential in improving customer experience and preventing fraud in e-commerce.
  • A/B testing and recommender systems are important tools in refining e-commerce websites.
  • Pricing optimization can boost profits by understanding customer price sensitivity.
  • Fraud prevention relies strongly on machine learning models for real-time decision-making.
  • Keeping up with new data science trends and continuous learning is vital for maintaining a competitive edge.

Deep Dives

Fraud Prevention in E-commerce

Fraud prevention poses a significant challenge in e-commerce, costing the industry a lot every year. Traditional rule-based systems often fail to adapt to the advanced tactics used by fraudsters. Elad Cohen highlighted Riskified's approach, which utilizes machine learning models to provide a chargeback guarantee. This ensures that me ...
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rchants are compensated for fraudulent transactions, thus reducing financial risk. "Machine learning is a lot more precise than simple rule-based solutions," Cohen noted. The process involves real-time analysis of transactions, enriched by data from various sources to enhance accuracy. Correct feature engineering and understanding of domain-specific details are important in creating effective fraud prevention systems.

A/B Testing and Multi-armed Bandit Algorithms

A/B testing is a potent method for assessing changes in e-commerce platforms, aiming to improve user engagement and conversion rates. Cohen explained the significance of hypothesis testing to gauge the effectiveness of changes, such as modifying website colors or layouts. Multi-armed bandit algorithms offer a more flexible approach, balancing exploration and exploitation to optimize results. "Companies that experiment faster generate more trials and see more alternatives," Cohen remarked. This repeatable process allows companies to continuously refine their strategies and achieve better performance metrics over time.

Recommender Systems

Recommender systems play a key role in personalizing the shopping experience and increasing sales. These systems use collaborative and content-based filtering to suggest products based on user behavior and product attributes. By analyzing purchase histories and product features, e-commerce platforms can present customers with items they are more likely to buy. This not only improves the shopping experience but also enhances retention and average order value. Cohen stressed the importance of utilizing both customer data and product similarities to create effective recommendations.

Pricing Optimization

Pricing optimization is a vital strategy for maximizing profits in e-commerce. By understanding the price sensitivity of customers, businesses can adjust prices to find the optimal balance between volume and margin. Cohen shared insights into how flexible pricing strategies, like those used by Amazon's Buy Box, can influence purchasing decisions. The goal is to offer competitive prices while maintaining profitability. "Merchants need to flexibly compare prices and adjust to market conditions," Cohen mentioned. This requires strong data analysis and a comprehensive understanding of customer behavior to make informed pricing decisions.

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