May 14, 2021

FinScan unveils Focus, new AI-Powered AML Screening Engine

AI
Technology
Data
Digitaltransformation
2 min
FinScan’s new matching engine, Focus, uses technology to produce explainable results for regulators and compliance officers

FinScan, a company that offers anti-money laundering (AML) compliance solutions, has announced the launch of its latest screening engine, Focus. 

According to the company, FinScan Focus integrates advanced AI, linguistic analytics, cultural anomalies, and data insights to ‘provide unprecedented levels of transparency and control to compliance officers looking to modernise their sanctions and politically exposed person (PEP) screening programs.’

“FinScan Focus helps compliance professionals address growing screening challenges through its immersive, predictive-analytics-based sandbox simulations,” said Kieran Holland, FinScan’s head of technical solutions. “These simulations remove the guesswork of how the changes will affect the number of alerts the organisation can expect to clear. And by giving compliance officers complete and granular control of understandable matching rules, FinScan makes it easy for them to explain their results to regulators.”

Randal Skipper, president of global field operations, FinScan said: “FinScan Focus combines our Data Quality heritage with our skills in computational linguistics to give clients a solution that models human behavior. Focus’s highly intuitive and configurable dashboards enable advanced monitoring and management of screening alerts, making it a huge breakaway from traditional technology and workflows.”

 

COVID-19 crisis and financial management 

FinScan recently conducted a global survey to see how the anti-money laundering (AML) and anti-financial crime (AFC) compliance communities are dealing with challenges brought on by COVID-19. 

The report found the top challenge facing the compliance community during the coronavirus pandemic was the need to work remotely from home while lacking the proper technology and access to internal IT systems. Most organisations feel that their exposure to compliance risk has increased due to, remote working, inadequate IT support for remote access, and the speed with which the organisation had to adjust to crisis management mode and roll out new online services.

63% of respondents expected that criminal activity levels would rise and that their ability to meet regulatory requirements would be impeded due to the pandemic situation. Many organisations said they do feel that they have some level of business continuity planning in place, but not sufficient planning to accommodate this particular pandemic situation. This shows there is an opportunity for future planning, to incorporate a more pandemic-related risk-based approach into their existing business continuity frameworks.

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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