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Highlighting Fraud with Artificial Intelligence

We have taken very careful design decisions all with the aim of enabling customers to address any type of fraud they face. That means we can ingest any data, generate any feature and provide you with the best machine learning models for identifying fraud and any rule. We have state of the art in-memory databases, algorithms, and low level code in our solution which makes it lightening fast whilst keeping the costs down. So what does a next generation AI solution look like?

Greg Hancell

25 Sep 2023

Current state of play

We’re in a transition period where society is almost expecting Artificial Intelligence services as part of  their daily lives. There is great value shown in uses such as the large language models of ChatGPT4.0. However in Fraud Prevention solutions there is hesitancy to trust machine learning models, due to many reasons:

  • Lack of understanding in how the model works
  • Lack of trust in the scores provided by the model
  • Lack of data available to train the model
  • Bad historical performance prior generations of fraud solutions using “AI” but with poor performance
  • Misconceptions generally around how Machine Learning works and its limitations

These are very valid reasons and you the reader are right to be cautious in using AI in fraud prevention solutions.

Today I aim to provide information that will help answer some of the questions you might have about AI and build trust in Lynx Fraud Prevention.

First let me explain that not all solutions that “utilise AI” or “use Machine Learning” actually use it, or at least use it in a meaningful way.

Ultimately, any solution that makes use of AI should be able to confidently perform a proof-of-concept and outperform any rules based solution. To give you an idea, Lynx Fraud Prevention outperforms known leading rules based solutions by a factor of up to 100.

That means 10 times less alerts with 10 time better accuracy, meaning we give you less alerts and catch more fraud. We will gladly prove this to you in a POC, as will any vendor that is truly using AI in their solution, as it’s simple to do.

So how do we utilize AI in the Lynx Fraud solution?

Next Generation Fraud Prevention

We have taken very careful design decisions all with the aim of enabling customers to address any type of fraud they face. That means we can ingest any data, generate any feature and provide you with the best machine learning models for identifying fraud and any rule.

We have state of the art in-memory databases, algorithms, and low level code in our solution which makes it lightening fast whilst keeping the costs down.

So what does a next generation AI solution look like?

  • Data agnostic approach meaning we can ingest any type of event, transaction, file you need to protect
  • Build any feature (intelligence points used by a model) meaning we have very comprehensive intelligence
  • Auto feature generation means we automatically classify data and generate relevant features.
  • Realtime in memory feature calculation meaning our models always have up to date data
  • Daily Adaptive Models meaning the models do not drift and stay relevant to new attacks and products.
  • Monitor the performance of the model to bring you confidence that it is outperforming the competition and massively outperforming any rules based solution.
  • Apply supervised learning, which outperforms unsupervised learning by up to a factor of 10.
  • Genuinely multichannel meaning we are not blindsided by cross channel attacks and are able to identify attacks traversing multiple channels.

Not only do we do this, we also return a result to the calling application in just 25 milliseconds and enable you to blend this score, if you wish into a rule.

This means you can use the score on its own, as many of our customers do. And/or if you want to, you can blend the score with rule logic to reduce the amount of false positives received from the rule. Remember this will reduce false positives by a factor of up to 100. That’s up to 100 times less work for you and your team for the same amount of fraud identified.

Legacy Solutions

What does a legacy AI solution look like?

  • Claim to use AI however they do not use labelled data, remember supervised learning can reduce false alerts by a factor of 10, versus unsupervised learning which doesn’t use labelled data. So, if you see another solution utilizes unsupervised learning, that’s up to 10 times more work for you and your team for the same amount of fraud. Unsupervised learning has it’s benefits for example in facial recognition, image recognitition, anomaly detection, nlp and other types of tasks where identifying data types and classifying data is onerous. However it should not be used as a main approach in fraud prevention, as it is inefficient as the main approach to identify fraud. Instead it can compliment a performant solution with additional atypical interaction and transaction identification.
  • Claim to train their models regularly but do not and need a data science team. We train our models every day and have been doing so with top financial institutions automatically for years. We do this without the need for a data scientist on your side. This means that we not only outperform other solutions claiming to use AI, but we you will be able to identify solutions that do not truly know how to apply machine learning i.e. those that require you to uplift your data every quarter and manually retrain your models.
  • Professional Services costs are kept low as you don’t have the professional services overhead or the data science cost on your side to keep the model relevant.
  • Rules based. This means that every new attack needs to be studied and identify how it beats the current rule set. Not only that but attackers are able to interpret rules and logic based on machine learning attacks and beat the rule set. Ultimately a rule set is not adequate to defend against todays advanced attacks.
  • Claim to continuously learn, yet when you review the data that the models are using you realize there is a delay of a day. Meaning you and your customers are vulnerable to attacks until the new data is available.

Attacks we can identify

Lynx fraud prevention can identify and stop the following attacks:

  • Merchant Fraud
  • Account Takeover
  • Authorised Push Payment Fraud (APPF)
  • Social engineering based attacks
  • Phishing victims
  • Skimmed cards
  • Replayed transactions
  • Stolen identity
  • And more

We do this by understanding your users better than the competition. We deeply understand the device, user behaviour, locations, travel, spend, patterns of interaction, their associated beneficiaries, how much money they typically transfer/ spend and when.

Why don’t you give us a try?

We were born out of data and data science. We live and breathe data, algorithms, insight and intelligence. We ensure that:

  • Our models are the best in the business
  • We reduce your costs by reducing false positives
  • We reduce fraud
  • We reduce the complexity of rule building
  • We improve job satisfaction and alert fatigue by giving you meaningful alerts
  • We continuously learn to changing attacks and new products / customer behaviour
  • We ensure our models are drift and attack resistant

We’re confident that we’re able to stop the attacks you face and have been doing so for the last 20 years. Don’t take our word for it have a look at other financial institutions that love our solution.

We’re the AI Solution you’ve been patiently waiting for since they came onto the scene.

So why don’t you reach out and ask for a proof of concept today, you won’t be disappointed.

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