Financial security

Protect your clients’ financial data

 

Prevent financial fraud


Machine learning is getting better and better at spotting potential cases of fraud across many different fields.
The main responsibility for providers of financial services is the protection of their clients against fraud. The
old ways of keeping their accounts secure are no longer sufficient. In order to protect clients’ data against
increasingly advanced threats, companies must stay one step ahead of hackers.

Machine learning helps prevent security breaches by out-thinking the criminals.


 
By comparing each transaction against those recorded in the account history, machine learning algorithms are able
to assess whether the transaction is actually a fraud. Suspicious activities, such as unusual purchases or large cash
withdrawals, alert the system that now has to delay the transaction until a human can make a decision. In many
cases a purchase or withdrawal attempt may be automatically declined by the system.
 
Next to fraud detection and prevention, machine learning can be applied to many different fields of financial services,
like algorithm trading, credit decisions, risk management, etc.
 

Financial security solutions by Nucleus Group


 

Nucleus Group can create a custom machine learning solution that will track anomalies and flag suspicious
behaviour. That way you can be sure your system and customers’ accounts are properly protected. 
 
With your needs and requirements in mind, we will come up with the best possible solution based on
machine learning algorithms in order to help your company and your customers. 
 
Our team of expert developers approaches all projects with dedication and professional bahavior so you
can rest assured your data will be in good hands. 

 
 

 

Want to know more?


For more information about financial security solutions, feel free to contact us and schedule a meeting with our experts.
We will be more than happy to meet with you and come up with the best solution for your business.

 

 

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