The Rise of AI in Fraud Detection: Securing Crypto Wallets and Exchanges

Crypto is dynamic, unstable, and yet international. It is also the pull of fraud.

Billions were lost to phishing mishaps, rug pulls, phoney trades, and wallet hacks alone in 2024. With the development of the industry, the strategies of bad actors also increase. Even simple firewalls and manual monitoring are no longer sufficient. Artificial intelligence (AI) comes in there.

Artificial intelligence is transforming the process of protecting digital assets. Whether it is warning of suspicious wallet activity to block fraudulent exchanges in real-time, it is becoming the ultimate security partner in the crypto space.

So, how is AI and generative AI in FinTech assisting in the security of crypto wallets and exchanges, etc? And why is it the game-changer?

Let us discuss.

Why Traditional Security Tools Fall Short

The transparency of blockchain has two sides.

On the one hand, all transactions are entered. Conversely, all is transparent, i.e., attackers can study wallets and smart contracts no less than defenders.

The old guard of fraud-prevention strategies, rule-based systems, fixed blacklists, and manual checks simply cannot keep up.

Such procedures are frequently:

• Unable to identify new patterns of fraud
• Make too many false positives
• Respond when it is too late

Crypto fraud and its changes are going at a pace that most centralised systems are not able to keep up with. There are bots, social engineering, and deepfake videos.

You require better tools- ones that know and evolve.

Learning the Patterns of AI in Fraud Detection

With the help of AI, particularly machine learning (ML), rules are not only followed. It learns behaviour and identifies aberrations.

Assume you own a wallet that has a weekly average of 0.05 BTC. Everything is going fine, and then all of a sudden, it broadcasts 5 BTC to an unknown location in Nigeria.

This would immediately be flagged by an AI-powered fraud detection engine that scrutinises the transaction and breaks it down according to the geolocation, wallet history, IP reputation, and token flow.

Better still, it might cause automatically preconditioned responses:

• Stay with the transaction
• Warning the wallet owner
• Inform exchange/custodian
• Cross-reference additional checks against identities

💡It is not science fiction; it is already in systems like Chainalysis, Elliptic, and TRM Labs.

How to Secure Crypto Wallets with AI

Crypto wallets are hot targets since that is where the assets reside.

Wallets, and in particular non-custodial wallets, put all control and responsibility in the hands of the user. In the event you lose your keys (or have them stolen, it is over. No bank to phone.

AI assists in the security of wallets in a number of ways:

1. Behavioural, biometrics

AI monitors the way an individual uses his/her wallet: typing, swiping, and device fingerprint.

The system can lock access when it wants to secure the login by another person in a far region with an alien pattern.

It is like having a Face ID, but for your interaction type.

2. Anomaly detection in real time

Rather than creating fixed thresholds (such as restricting the number of withdrawals to one per day), AI creates an individual profile of each user.

It is familiar with how you are supposed to look--and sends up warning signals when you cannot.

This will guard you against malware or phishing attacks in which the hacker has your own credentials.

3. Phishing prediction 

The AI tools could peruse the web and the dark web to identify fake wallet applications, like-named domains, and malicious browser extensions.

They are able to infer upon new phishing threats even prior to clicking because they crawl thousands of sites every minute.

Other wallets, such as MetaMask and Trust Wallet, are beginning to build such functionality in.

AI in Crypto Exchanges

Another challenge is the scale faced by the crypto exchanges.

There may be millions of trades a day in a single exchange across thousands of assets. Add withdrawals, KYC checks, token listings, and what do you get? A perfect environment that allows fraud.

AI provides a number of potent tools to assist.

Transaction monitoring

AI does not work with fixed rules of anti-money laundering since it constantly searches transaction history in an attempt to identify any manifestations of layering, mixing, or suspicious trends of withdrawals.

Giving an example, when the user adds large quantities of Monero and then rapidly exchanges it to stablecoins and sends them to different wallets, the system may consider that as an operation of money laundering and freeze the transaction.

Identity verification

Facial recognition and document scanning with the help of AI would make onboarding faster, whilst alerting to counterfeited IDs. Some systems can even detect the slightest hints of deepfake tampering, such as awkward eye blinking or light breaking.

Binance, Kraken, and other exchanges are already using AI-powered KYC solutions to onboard millions of users in a KYC-compliant manner.

Suspected insider threat detection

It is also possible that AI should monitor suspicious employee activity.

When a member of the finance department logs into machines on the production environment in the middle of the night on a different device, it emits a red warning flag. AI records behavioural patterns over time and rates the risks they pose, and through these, the companies can identify risks occurring within their company.

Possible Constraints

Naturally, AI itself is not perfect.

It requires good data to learn from, and in crypto, that can be hard.

Blockchains are open, yet the identities of users are not.

Some wallets contain the funds of tens of people. The behaviour of some tokens differs from that of others.

There are also false positives. It will make users frustrated when flagging too many good transactions.

The right balance between safety and convenience is highly important.

And there is the threat of malevolent AI. Hackers are able to develop their models to conceal themselves. It is an arms race, and both are growing rapidly.

Nonetheless, as it will always come with refinement and have proper human supervision, AI will continue to be the most effective backstop we have.

The Future Perspective of AI in Fraud Detection

AI has only begun to work in crypto fraud detection.

We are even shifting into systems that not only respond, but also anticipate- prevent fraud even before it occurs. For instance:

• Wallet-level predictive risk scoring before a transaction
• Multi-chain behavioural Measurement in Ethereum, Solana, and more

Smart contracts in fraud response. Autonomous fraud responses are defined as smart contracts that will not be asked questions but will be activated after detecting potential fraud.

Even on-chain AI oracles, collections of AI tools running in the blockchain to impose trust without a central authority, are being considered in some blockchain networks.

The same technology can be used alongside zero-knowledge proofs and high-security encryption to perhaps realize a day when security does not disrupt privacy or decentralization.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of Obiex.

This content is for informational purposes only and should not be considered financial or investment advice.

Readers are encouraged to conduct their own research before making any financial decisions. Obiex is not responsible for any outcomes resulting from the use of this information.