Good AI Vibes #7
Enhancing Same-Day Deliveries, Fortifying IoT Attack Detection, and Outsmarting Car Insurance Fraud
π Hello and a warm welcome to the seventh edition of Good AI Vibes! We're elated to have both new readers and our loyal subscribers with us on this journey into the dynamic world of artificial intelligence.
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In every issue, we present three distinctive AI applications that are redefining industries and addressing intricate business challenges. Each story offers a snapshot of the issue, the AI-powered solution tailored to resolve it, and the meaningful results reaped.
π£ We're pleased to share that from this issue onwards, as part of our commitment to delivering value and staying relevant to your needs, we are standardizing the industry and business function categories. Stay tuned till the end to discover more about this enhancement. π β¬οΈ
In this issue of Good AI Vibes, we're delighted to bring you the following: π€
π Solving the Same-Day Delivery Puzzle (Industry: Retail, Consumer Services & Travel / Business Function: Supply Chain, Logistics & Sustainable Operations)
π‘ Edge-ing Out Threats: Revolutionizing IoT Security (Industry: Technology, Media & Telecommunications / Business Function: Technology Advancement & Digital Transformation)
π Hitting the Brakes on Fraud: How AI Revolutionized Car Insurance Claims (Industry: Financial Services / Business Function: Financial Management, Risk & Procurement)
We hope that these use cases will kindle innovative ideas, inspiring you to ponder on how AI could be incorporated into your products or services. π‘
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And finally, as we continually strive to enhance your reading experience, a delightful new section awaits you at the end of this issue. π₯³ Prepare to see 'Good AI Vibes' from a completely different angle. But we won't spoil the surprise - you'll have to discover it for yourself! π
Now, let's plunge into this issue's AI use cases that are reshaping industries far and wide.
Wishing you a happy and insightful read!
Solving the Same-Day Delivery Puzzle
What was the major hurdle?
Richard, a seasoned operations manager at a prominent e-retail company, was facing a monumental challenge. The era of same-day delivery, although popular among the customers for the convenience it provided, was a Pandora's box for providers. The behind-the-scenes story was much different from the rosy picture of prompt delivery painted in the public domain. The pressing issue was efficiently managing capacity for high-value orders, planning tours that ensured profitability, and dealing with the spiraling overhead costs. These problems were intricacies woven into the fabric of their day-to-day operations, and the juggling act was growing increasingly complex.
How did they take on this challenge?
Ever pragmatic, Richard sought to adapt to these evolving circumstances. He put together a strategy that involved a careful tweaking of the combination of delivery spans and prices presented to each incoming customer request. The objective was clear: to secure a reserved capacity for high-value orders and subtly influence customer choices towards more efficient delivery operations. But was this strategy enough? Was it robust to withstand the test of an ever-dynamic e-retail environment?
Why weren't traditional methods cutting it?
Richard's approach was not without merits, but it failed to resolve all the issues on the table. The strategy was undergirded by a set of assumptions that didn't always hold water in the face of the rapidly changing e-commerce landscape. One significant gap was the absence of anticipatory planning. The incapacity to accurately forecast incoming high-value orders often led to mismanaged resources, ineffective tour planning, and consequently, profit margins that were far from satisfactory. Despite his best efforts, the solution he adopted was more reactive than proactive.
How did AI change the game?
The advent of AI marked a sea change in Richard's approach. By integrating AI into his strategy, he was able to include an anticipatory sample-scenario based value approximation into his demand-management approach. This meant he could now forecast high-value orders more accurately, ensuring efficient capacity management. It also enhanced the tour planning process, making it more in line with the operational realities of same-day delivery. The results were nothing short of remarkable - a staggering 50% increase in the contribution margin when pitted against a myopic benchmark approach. AI's entrance was not just an incremental step in operations; it marked a paradigm shift in how Richard's company tackled same-day delivery challenges.
Main reference: Klein, V., Steinhardt, C. (2023). Dynamic demand management and online tour planning for same-day delivery. European Journal of Operational Research, 307(2), 860-886.
See other references at the end.
Edge-ing Out Threats: Revolutionizing IoT Security
What was the major hurdle?
Meet Liam, a dedicated cybersecurity officer in a tech-savvy company. Liam had a huge problem on his plate: the exponential growth of Internet of Things (IoT) devices. Each of these devices - be it an environmental monitor or an on-demand electrical switch - added another potential vulnerability to the company's network. The threat landscape was continually expanding, and with traditional cybersecurity measures failing to keep up, Liam was in a bind.
How did they take on this challenge?
To combat this, Liam deployed a centralized detection system designed for operational technology systems. This system, while effective in theory, had its own challenges. It struggled to keep up with the heterogeneity of IoT devices and the infrequent updates they received, leading to gaps in the defense line.
Why weren't traditional methods cutting it?
The centralized model had another serious flaw: the constant transfer of data from the network edge (where data is born) to the central server posed significant privacy and security risks. In the worst-case scenario, if the central server was breached, the entire network could be compromised.
How did AI change the game?
Enter AI. A paradigm-shifting solution came in the form of a federated-based approach employing a deep autoencoder for attack detection. This method brought machine learning computation to the edge layer, right where the data was generated, instead of transferring the data off the network edge. Not only did this significantly enhance data privacy and security, but it also resulted in a significant improvement in the accuracy rate of attack detection - a whopping 98%! The transformation was nothing short of revolutionary for Liam and his company. The IoT devices, which were once a security nightmare, became manageable and significantly more secure, and all without compromising the privacy of the data they held.
Main reference: Regan, C., Nasajpour, M., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., Choo, K. R. (2022). Federated IoT attack detection using decentralized edge data. Machine Learning with Applications, 8, 100263.
See other references at the end.
Hitting the Brakes on Fraud: How AI Revolutionized Car Insurance Claims
What was the major hurdle?
John, a seasoned insurance investigator, had his hands full. The weight of $40 billion in annual vehicle insurance fraud loomed over his company and the industry at large. This pervasive issue was driving up the premiums for their customers, costing the average U.S. family an additional $400 to $700 each year. John knew that a sizeable chunk of these fraudulent claims came from a devious practice: perpetrators dismantling damaged car panels from one vehicle, reassembling them on another, and then claiming insurance on the second car.
How did they take on this challenge?
Being a veteran in the field, John and his team were well aware of these tricks. They had a protocol in place: for each claim, they painstakingly sifted through images, extracting basic vehicle information and identifying any damage. Next, they cross-referenced these damages against previously processed claims, checking if the so-called 'new' damage was indeed new. It was a daunting task, yet one that John's team took on with relentless commitment.
Why weren't traditional methods cutting it?
Despite their best efforts, John's traditional approach was falling short. Labor-intensive and time-consuming, the manual system was vulnerable to human error and couldn't scale in the face of the growing sophistication of fraud techniques. The inaccuracies, characterized by an uncomfortably high rate of false positives, proved costly and frustrating. The harder John's team worked, the clearer it became that their approach was not sustainable. They needed a more efficient solution - and fast.
How did AI change the game?
The game-changer arrived in the form of a deep learning-based anti-fraud system. This end-to-end solution revolutionized John's approach to investigating insurance claims. Leveraging AI, the system autonomously analyzed claim images, identified bodywork damage, and crucially, confirmed whether the damage had already been processed in previous claims.
The results were breathtaking. Compared to other state-of-the-art image similarity models, John's new AI-powered ally topped them by a solid 15.27% in terms of mean average precision. The false positives, once a thorn in John's side, decreased by up to 18%. Even better, the AI system reduced potential alerts by a staggering 72%. With this innovative tool at his disposal, John was finally equipped to efficiently tackle insurance fraud, leading to substantial savings for his company and their customers.
Main reference: Maiano, L., Montuschi, A., Caserio, M., Ferri, E., Kieffer, F., GermanΓ², C., Baiocco, L., Ricciardi Celsi, L., Amerini, I., Anagnostopoulos, A. (2023). A deep-learningβbased antifraud system for car-insurance claims. Expert Systems with Applications, 231, 120644.
See other references at the end.
Good AI Vibes Cartoons π
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Feast your eyes on our inaugural jest. π
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Thank you for joining us on this issue of Good AI Vibes! We hope that our selection of AI use cases has sparked innovative thoughts and presented a clearer picture with our new standardized industry and business function categories. If you haven't already, make sure to check out the details about these categories at the end of this newsletter. β¬οΈ
We also hope you enjoyed the surprise at the end of the issue. This delightful addition is another step in our journey to make AI more relatable and engaging.
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Until next time, keep the Good AI Vibes flowing! See you soon! π
βΉοΈ As mentioned at the beginning, our new categories for a more streamlined reading experience are as follows. From this issue onwards, you'll see our use cases organized under these six industry categories and six business function categories:
Industry Categories:
Industrial Manufacturing, Transportation & Logistics: Includes automotive, chemicals, metals & mining, paper & packaging, semiconductors, aerospace & defense, and industrials, as well as transportation and logistics services.
Financial Services: Covers financial institutions, insurance companies, private equity & principal investors, and other service providers managing, investing, or lending funds.
Energy & Built Environments: Emphasizes on sectors like electric power, natural gas, utilities, oil & gas, engineering, construction, building materials, and real estate.
Technology, Media & Telecommunications: Centers on technology development and services, media production and services, and telecommunications.
Retail, Consumer Services & Travel: Encompasses retail, e-Commerce, travel & tourism, consumer packaged goods, and other directly consumer-facing services.
Public Sector & Essential Services: Includes education, healthcare, public sector organizations, urban infrastructure, and other social and public services.
Business Function Categories:
Marketing, Sales & Customer Relations: Covers marketing strategies, sales operations, customer insights, pricing, revenue management, and overall customer experience.
Operational Efficiency & Production: Includes efficiency measures, quality control, maintenance, and manufacturing operations.
Supply Chain, Logistics & Sustainable Operations: Focused on managing the movement of goods and services within an organization, incorporating sustainable practices into these processes.
People Management & Organizational Development: Covers aspects of managing an organization's human resources and driving overall organizational performance.
Financial Management, Risk & Procurement: Involves managing the organization's finances, assessing and mitigating risks, building resilience, and overseeing procurement and cost management.
Technology Advancement & Digital Transformation: Focuses on the integration and management of technology and data within an organization and leading digital transformation initiatives.
We trust these well-defined categories will enhance the relevance and clarity of our AI stories, helping you quickly locate the insights that matter most to your industry or function.