Combining AI & Blockchain Data for Predictive Analysis, Fraud Prevention, and More

How applying AI and blockchain data with Snowflake can drive efficiency and efficacy. Written by the Flipside Crypto team.

Section Outline

• AI and Blockchain Data: a Powerful Pairing
• Predictive Analysis
• User Modeling and Fraud Prevention
• Audience Targeting and Advertising
• Next Steps

AI and Blockchain Data: A Powerful Pairing

As general-purpose tools like LLMs rapidly improve and techniques for training models in-house mature, a bit of a data science Renaissance has begun. It coincides with a similar movement in Web3 — blockchain use has grown exponentially for nearly a decade as infrastructure has scaled to support ever more consumer-facing applications.

And with Snowflake, organizations can now make use of both. Leveraging the two technologies by applying AI-based data analysis to blockchain data can significantly augment decision-making processes, consumer power, and investing, security, and scalability practices.

This article explores how integrating AI with blockchain data using Snowflake can efficiently and conveniently deliver cutting-edge solutions across fields like predictive analytics, risk management, and fraud detection/prevention, even for organizations not directly involved in Web3.

Skip the line

In this article, we explore high-level concepts you can then apply creatively in an organization using Snowflake. If you just want to get straight to how AI and blockchain data can expand your revenue, user base, and security, then let’s talk about you.

Or, email Flipside at data-shares@flipsidecrypto.com to have their Web3-native team of experts design a custom strategy for your business to leverage blockchain data (or to just chat about data!).

Now, let’s get to the details.

Predictive Analysis

Predictive analysis aims to forecast future events by analyzing current and historical trends. Blockchain data can be fed into machine learning models to predict patterns and trends, and because blockchain data is immutable and historically complete, it’s primed for consistent analysis from any angle.

Comprehensive blockchain data, with added off-chain context and human-readable architectures like you’ll find in Flipside data with Snowflake, are particularly convenient for use alongside LLMs, as it requires minimal prep and interpretation, and can be activated with natural language.

For instance, in decentralized finance (DeFi), cross-chain swap data can be used to train ML models to predict investing trends across the entire industry, which can then be applied to portfolio management, risk-off alerts, economic research, and more. Similarly, all forms of user activity are immortalized on the blockchain, which means that all forms of user activity may be analyzed and predicted by AI.

As an example, Flipside built NFT Deal Score, an app that pairs ML with blockchain data (available through Snowflake Data Shares) to determine the expected value of NFT collections and their various traits and trends.

Because many of the chain foundation partners have both user acquisition and NFT activity goals, Flipside’s team is able to help them apply analyses from trading data to business intelligence and user engagement strategies.

User communities also began using and sharing the tool as justification for their own trades, and for finding undervalued NFTs (which has a strong analog for arbitrage opportunities in traditional investing). This highlights how AI can open new avenues for enterprises to attract interest in their tooling. If an organization leverages AI internally, it may also be able to create new business models by exporting it.

User Modeling and Fraud Prevention

Applying machine learning to blockchain data can also be used for both user modeling and fraud prevention. Because all on-chain activity is recorded immutably and accessible in near real-time via Snowflake, machine learning models can build user models to identify both “good” and “bad” activity.

To target and incentivize “good” activity, the Flipside team built an app called Trails to identify and incentivize target behaviors; such as performing specific actions in an app, or even attracting highly-convertible users to an app. By analyzing wallet activity over time, we were able to segment audiences based on their interests and activity into groups most likely to perform desired actions, and incentivize them accordingly.

Alternatively, AI can support real-time monitoring of on-chain activity for fraud detection in digital payments, for example; an AI trained on transaction patterns parses blockchain data for statistical and contextual anomalies, reporting on potential fraud attempts. With historical blockchain data available in most cases to the genesis block (or, the first block on a blockchain) with Snowflake, even the oldest activity can be exhumed and analyzed. Thanks to AI, no on-chain fraud is safe, no matter how much time has passed.

LLMs are particularly useful in cases of fraud prevention as NLP can link user patterns to profiles and much more accurately filter for out-of-character behaviors. In many cases, the AI can handle not just identification, but also resolution, by contacting users and moderating cases with minimal need for oversight.

In cybersecurity applications, app and web traffic can be monitored at scale with low cost, and security practices can be routinely (or even continually) AI tested and updated. As AI improves, hacking, botting, and other malicious practices become easier. However, so does their defense. Because of this, it’s important to be proactive in the adoption of AI for security.

Audience Targeting and Advertising

Blaze AI is a tool that uses Flipside’s comprehensive blockchain data through Snowflake to analyze consumer trends and wallet activity.

Its AI links activity across platforms, and is able to connect a user’s presence on social media to their wallet address(es), topical interests, and purchasing patterns. It’s a great example of how audience targeting for media and advertising is improved when measured with blockchain data.

Even traditional organizations looking to build more complete demographic data can benefit from more detailed user profiles and tailor advertising content accordingly. Similarly, for organizations looking to break into Web3, AI-based blockchain data analysis of this caliber can separate success from failure.

Next Steps

With Snowflake, blockchain data can be a resource even to organizations not operating in Web3 — and with direct queries and no complex pipelines, leveraging blockchain data is easier than ever.

To get started, you can try Flipside’s comprehensive datasets free for 14days, and get customized support just by contacting them at https://data.flipsidecrypto.xyz.