Business

Theft-Proofing Retail: How TRACARTS Revolutionizes Cart Security

Published

2 years ago

July 29, 2024

The problem of shopping cart theft is as old as the shopping cart itself. According to the Food Marketing Institute, approximately 2 million shopping carts are stolen or simply go missing every year, leading to substantial monetary losses for retailers that trickle down to the everyday consumer.

The escalating cost of living is a growing concern not only for consumers but also for retailers. While stolen carts may not be making regular headlines, they are a costly issue that threatens to worsen the economic strain that has already taken hold, with entire municipalities now getting involved in the missing cart problem.

Recently, the city of Fayetteville, North Carolina, earmarked $78,000 of taxpayer funds to tackle the cart problem and used the money to round up as many missing carts as possible over a two-year period. Albuquerque, New Mexico, ran a similar program, retrieving over 1,800 carts in just two months.

Missing and stolen carts create economic hardship, issues for consumers, and blight for cities. Thankfully, TRACARTS is a company stepping forward with a technology-informed solution, working to significantly reduce the number of carts that are stolen or otherwise go missing every year and save retailers time and money.

The cost of wayward carts

With each cart taken from a retailer, that retailer stands to lose upward of $180. Yet the millions of carts that go missing each year are a significant hit to not only the retailer but also the shoppers because when retailers seek to recoup funds lost due to missing and stolen carts, they are often forced to raise their prices.

TRACARTS has considered the human element of the cart problem with its system. In fact, the psychological aspect of the TRACARTS system is likely why it works so well.

“There are those retail stores that protect their carts by charging money from customers, who get a refund once their cart is returned,” says Chaya Grosinger, Chief Administrative Officer for TRACARTS. “In the U.S., this security measure is utilized with a quarter. If you wanted a cart, would you be willing to spend or lose 25 cents for a cart that costs upwards of $180?”

There is also an altruistic side to the cart problem that TRACARTS is leveraging. Recently, the question of whether one returns a cart after use has become a social media test of moral righteousness. The general consensus seems to be that “good people” return carts, while those who don’t face society’s harsh judgment.

This litmus test is part of TRACART’s multifaceted approach to solving the missing cart problem. TRACARTS is betting on the good feeling that comes from returning one’s cart.

A tech-informed solution

Along with psychology, TRACARTS is also using technology to address the issue of missing and stolen carts. They know that the psychological pull of a “good deed” may not be sufficient to truly address this costly issue, so the solution must be multifaceted.

The user-friendly TRACARTS system makes it easy to track and secure shopping carts while they’re in use and when they are returned. The TRAC hub — a series of customizable shopping cart trains arranged in one, two, or three multidirectional lines — can be installed in any retail store’s parking lot. Strategic placement of the TRAC hub allows for easy access to the carts without having to weave through parked cars.

When the carts are not in use, the TRAC system locks them into place. They can be released through the system’s smart technology program, with the TRAC kiosk acting as the customer interface that dispenses or accepts them. Shoppers can use a White Label app, a fob, a PIN code, or a phone number, among various other identification forms, to release the carts.

Returning the carts is hassle-free. Shoppers simply place the carts back into the TRAC hub without further interaction with the kiosk.

The integration of smart technology is another facet of the TRACARTS system that makes it stand apart from other solutions. It provides retailers with valuable data analytics and ways to engage with their shoppers. TRAC dashboard is entirely customizable, allowing it to meet the specific needs of different retailers depending on what date they are interested in collecting. Shoppers are incentivized to return their carts and given access to special promotions, such as VIP discounts and rewards, culminating in a positive shopping experience for both the shopper and the retailer while also gamifying the cart return process.

While the issue of stolen and missing carts will not vanish overnight, TRACARTS is deploying advanced technology, social consciousness, and psychology to help retailers save time and money. As more retailers realize that this problem will not simply go away on its own, they will turn to solutions like TRACARTS to help them manage their cart inventory and make shopping at their stores a more pleasant experience.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

1 day ago

April 16, 2026

Matthew Wiles

AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

Identify non-linear relationships between asset classes that correlation matrices do not capture
Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
Flag concentration risks before they appear in standard risk reports
Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.