As long as banks have been operational, money laundering and illicit fund transfers have been a bane of the industry. As our world grows increasingly digital, the importance of Anti-Money Laundering (AML)
Anti-Money Laundering (AML)
Anti-money laundering (AML) is a term that describes laws, processes, and regulations that are intended to prevent illegally obtained funds from being disguised as income gained through legitimate means. The fundamental purpose of the AML laws is to help safeguard, detect, and report suspicious activity including the predicate offenses to money laundering and terrorist financing, such as securities fraud and market manipulation.Most exchanges have AML measures that include identity verification
Anti-money laundering (AML) is a term that describes laws, processes, and regulations that are intended to prevent illegally obtained funds from being disguised as income gained through legitimate means. The fundamental purpose of the AML laws is to help safeguard, detect, and report suspicious activity including the predicate offenses to money laundering and terrorist financing, such as securities fraud and market manipulation.Most exchanges have AML measures that include identity verification
Read this Term) controls in big banks has grown.
As a result, criminal sophistication has increased, and many banks have struggled to keep pace with the arms race.
KPMG reports that the average bank in the United States spends $48 million on AML alone, with big banks spending as much as $500 million per year.
However, big data Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Read this Term technology is revolutionizing the way banks go about enforcing compliance these days.
Here are some of the most significant impact areas where analytics is changing AML detection and compliance.
Connecting and Analyzing Multiple Data Sources
Data is the key to effective AML procedures. Legacy compliance processes require multiple clarifications and touchpoints between compliance employees and clients.
In today's world of seamless integration and instant deliveries through mobile apps, such processes are out of step and create poor consumer experiences.
The need for multiple touches with customers arises thanks to the difficulty of gathering data and integrating it into a single platform.
Legacy systems process data in specific formats and often cannot speak to other systems. This means compliance employees rely on IT departments to clean and format data and manually integrate information into proprietary reports.
These days, analytics platforms can connect data from disparate sources and process them into understandable formats automatically.
The typical money laundering transaction involves chains of data, often involving shell companies in jurisdictions that have varying degrees of compliance, and creative accounting techniques.
Manual transaction matching is an almost impossible task in these situations, even before one considers the data cleaning and preparation process.
Analytics platforms remove this hurdle completely and allow compliance employees to create ad-hoc reports based on the data they believe is significant.
As a result, transaction and approval processes don't require frustrating phone calls with clients and multiple touches.
Enhanced Data Visualization
The implications of data are far easier to grasp when viewed in a visual format. Business analytics software platforms can help AML departments create data representations that communicate conclusions in easily understood terms.
These platforms also allow employees to drill deep into data and to slice and dice it as they deem fit. Most platforms come equipped with parameterized or template reports.
However, employees can choose to follow data trajectories they deem significant. Given the increasingly "creative" nature that money laundering has adopted in recent times, giving employees the flexibility to exercise discretion is a key part of AML compliance procedures.
Creating visually rich reports is as simple as dragging and dropping data points. These reports can be shared across the organization easily and thanks to increased collaboration, decision-making becomes efficient.
Every report can be drilled into and data from multiple reports can be combined into interactive views.
There is a growing trend of incorporating predictive analytics that uses machine learning algorithms to track patterns and predict future patterns.
Even without the aid of ML, analytics platforms can alert users to abnormal trends and present data for approval.
Real-Time Monitoring
Thanks to multiple data sources being integrated onto a single platform by dragging and dropping, AML departments can now monitor data and receive insights as they happen.
Ongoing issues can be monitored easily thanks to data democratization. If everyone in the organization can instantly view a visually rich dashboard, there is no need for update requests or to rely on IT departments to clean data.
As a result, AML departments can react faster to ongoing transactions that present threats to the organization. The ability to gain insight isn't restricted to a single device.
Most BI analytics platforms are mobile-friendly and have alert mechanisms. Compliance teams can automate compliance decisions based on data thresholds and they can select specific transactions for manual review.
Real-time monitoring also allows compliance teams to react to potential fraud quickly and mitigate issues before they get out of hand.
Given the potential reputational damage a bank can sustain from illegal transactions, the ability to react quickly is invaluable.
Ease of Use
The biggest advantage that BI analytics platforms bring to an organization is their ease of use. Legacy platforms required users to have the ability to query databases using programming languages.
As a result, business users were heavily reliant on their IT teams to run reports and submit them on time.
With business users having to constantly translate business requirements into technically friendly ones, valuable time was lost. To mitigate this, teams would develop parameterized reports.
However, this was a static approach to a dynamic environment. As the pace of digital adoption grew, data points in these static reports grew obsolete faster than teams could keep pace with.
BI analytics these days are extremely user-friendly. IT involvement is restricted to the initial setup. Business users can drag and drop data points as they deem necessary to create reports that can be shared and understood across their organization.
Giving non-technical users the ability to query big data has proved powerful.
According to Mckinsey & Company, the average decision time to develop a go/no-go decision regarding a high-risk client has reduced to 72 hours.
This used to be the time frame to approve a zero-risk client in the past. There's no better illustration of how powerful analytics are to AML departments.
Better Compliance, Safer Transactions
Malicious actors around the world are using digital platforms to cloak their activities. It's essential for compliance teams to get up to speed with these methods and BI analytics platforms give them the ability to do so easily.
As long as banks have been operational, money laundering and illicit fund transfers have been a bane of the industry. As our world grows increasingly digital, the importance of Anti-Money Laundering (AML)
Anti-Money Laundering (AML)
Anti-money laundering (AML) is a term that describes laws, processes, and regulations that are intended to prevent illegally obtained funds from being disguised as income gained through legitimate means. The fundamental purpose of the AML laws is to help safeguard, detect, and report suspicious activity including the predicate offenses to money laundering and terrorist financing, such as securities fraud and market manipulation.Most exchanges have AML measures that include identity verification
Anti-money laundering (AML) is a term that describes laws, processes, and regulations that are intended to prevent illegally obtained funds from being disguised as income gained through legitimate means. The fundamental purpose of the AML laws is to help safeguard, detect, and report suspicious activity including the predicate offenses to money laundering and terrorist financing, such as securities fraud and market manipulation.Most exchanges have AML measures that include identity verification
Read this Term) controls in big banks has grown.
As a result, criminal sophistication has increased, and many banks have struggled to keep pace with the arms race.
KPMG reports that the average bank in the United States spends $48 million on AML alone, with big banks spending as much as $500 million per year.
However, big data Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Read this Term technology is revolutionizing the way banks go about enforcing compliance these days.
Here are some of the most significant impact areas where analytics is changing AML detection and compliance.
Connecting and Analyzing Multiple Data Sources
Data is the key to effective AML procedures. Legacy compliance processes require multiple clarifications and touchpoints between compliance employees and clients.
In today's world of seamless integration and instant deliveries through mobile apps, such processes are out of step and create poor consumer experiences.
The need for multiple touches with customers arises thanks to the difficulty of gathering data and integrating it into a single platform.
Legacy systems process data in specific formats and often cannot speak to other systems. This means compliance employees rely on IT departments to clean and format data and manually integrate information into proprietary reports.
These days, analytics platforms can connect data from disparate sources and process them into understandable formats automatically.
The typical money laundering transaction involves chains of data, often involving shell companies in jurisdictions that have varying degrees of compliance, and creative accounting techniques.
Manual transaction matching is an almost impossible task in these situations, even before one considers the data cleaning and preparation process.
Analytics platforms remove this hurdle completely and allow compliance employees to create ad-hoc reports based on the data they believe is significant.
As a result, transaction and approval processes don't require frustrating phone calls with clients and multiple touches.
Enhanced Data Visualization
The implications of data are far easier to grasp when viewed in a visual format. Business analytics software platforms can help AML departments create data representations that communicate conclusions in easily understood terms.
These platforms also allow employees to drill deep into data and to slice and dice it as they deem fit. Most platforms come equipped with parameterized or template reports.
However, employees can choose to follow data trajectories they deem significant. Given the increasingly "creative" nature that money laundering has adopted in recent times, giving employees the flexibility to exercise discretion is a key part of AML compliance procedures.
Creating visually rich reports is as simple as dragging and dropping data points. These reports can be shared across the organization easily and thanks to increased collaboration, decision-making becomes efficient.
Every report can be drilled into and data from multiple reports can be combined into interactive views.
There is a growing trend of incorporating predictive analytics that uses machine learning algorithms to track patterns and predict future patterns.
Even without the aid of ML, analytics platforms can alert users to abnormal trends and present data for approval.
Real-Time Monitoring
Thanks to multiple data sources being integrated onto a single platform by dragging and dropping, AML departments can now monitor data and receive insights as they happen.
Ongoing issues can be monitored easily thanks to data democratization. If everyone in the organization can instantly view a visually rich dashboard, there is no need for update requests or to rely on IT departments to clean data.
As a result, AML departments can react faster to ongoing transactions that present threats to the organization. The ability to gain insight isn't restricted to a single device.
Most BI analytics platforms are mobile-friendly and have alert mechanisms. Compliance teams can automate compliance decisions based on data thresholds and they can select specific transactions for manual review.
Real-time monitoring also allows compliance teams to react to potential fraud quickly and mitigate issues before they get out of hand.
Given the potential reputational damage a bank can sustain from illegal transactions, the ability to react quickly is invaluable.
Ease of Use
The biggest advantage that BI analytics platforms bring to an organization is their ease of use. Legacy platforms required users to have the ability to query databases using programming languages.
As a result, business users were heavily reliant on their IT teams to run reports and submit them on time.
With business users having to constantly translate business requirements into technically friendly ones, valuable time was lost. To mitigate this, teams would develop parameterized reports.
However, this was a static approach to a dynamic environment. As the pace of digital adoption grew, data points in these static reports grew obsolete faster than teams could keep pace with.
BI analytics these days are extremely user-friendly. IT involvement is restricted to the initial setup. Business users can drag and drop data points as they deem necessary to create reports that can be shared and understood across their organization.
Giving non-technical users the ability to query big data has proved powerful.
According to Mckinsey & Company, the average decision time to develop a go/no-go decision regarding a high-risk client has reduced to 72 hours.
This used to be the time frame to approve a zero-risk client in the past. There's no better illustration of how powerful analytics are to AML departments.
Better Compliance, Safer Transactions
Malicious actors around the world are using digital platforms to cloak their activities. It's essential for compliance teams to get up to speed with these methods and BI analytics platforms give them the ability to do so easily.
