Cloudera: Taking a unified approach to fighting financial crime
As we've seen in Australia, financial crime has become increasingly pervasive and permeates all levels of the financial services industry.
Globally, financial institutions spent $1.28 trillion in the last 12-months combating financial crime.
It's hugely costly and the impact on business is significant with revenue lost due to financial crime estimated to be $1.45 trillion over the same period.
Customer confidence is also taking a hit with people naturally worried about how safe their personal and financial data is.
We know the cornerstone foundations of any successful financial provider is based on trust and reputation.
It can take years to build a trustworthy reputation and it can be eroded almost instantly if adequate strategies and due diligence behaviours are not enforced.
For some businesses taking measures to fight financial fraud, it can often feel like two steps forward and one step backwards as criminals become more creative, connected and collaborative - ready to exploit any opportunity inside or around the edges of businesses.
We're certainly seeing news ways of committing fraud to play out in the media including synthetic identity fraud, where a real and fake identity are combined making them harder to detect.
Fortunately, the toolbox to combat financial crime and protect data is also sharpening, assisting those tasked with analysing, protecting, processing and understanding data with the ability to have a holistic view of customers across various functions.
We're also seeing greater optimisation of business processes with this approach by combining the rationalising of governance, eliminating unneeded activities, streamlining and strengthening of processes with the digitising and deploying of advanced analytics which is enabling businesses to embed automated real-time (or near-real-time) financial crime controls within their core processes.
These approaches are reducing control failures, reducing false positives and making far more efficient use of valuable resources.
The importance of consistency when it comes to data center protection
A common question we are often asked by businesses is ‘where I should store my data?'
Many manage it in their own data centers while also working in the cloud.
But more businesses are seeking one consistent picture of their data regardless of where it happens to live – Amazon Web Services, Microsoft Azure, Google Cloud or their own data center.
Their driver is a need for consistent security, governance and one view of data across the spectrum, regardless of which data cloud or hub they are using.
That's why the next generation financial crime platform is not a software solution but an institution-wide data and analytics journey.
This requires investments in financial crime to be viewed from an enterprise perspective.
Doing so enables new insights into customer behaviour that supports a more personalised approach in delivering innovative products and services to customers.
The ability to automate early detection and monitoring systems is at the heart of next-generation financial crime prevention platforms supported by the latest machine learning and artificial intelligence algorithms.
To work optimally they need a high-quality data environment that not only prevents financial crime but provides businesses with a more holistic view of its customers, enabling it to develop and deliver more personalised products and services.
Think of this approach as both a grow and a defend strategy.
The role of data analytics and machine learning
The latest and most effective approach to combating financial crime is proving to be the combining of machine learning techniques with a more holistic, enterprise view of customer centricity through the integration of data from internal and external sources, typically referred to as ‘alternative data'.
At Cloudera, we encourage our customers to take a more unified view of financial crime through the use of a financial crime data lake.
We support businesses with a 100% open source enterprise data cloud platform that unifies and powers all data and machine learning workloads across hybrid and multiple cloud platforms with common security and governance.
United Overseas Bank uses a wide range of Cloudera suite of tools and services in support of their artificial intelligence and data science roadmap to drive the adoption of AI initiatives.
One of their implementations included a suite of advanced machine learning capabilities that help analysts conduct AML surveillance more efficiently by greatly reducing the amount of false positives in generated alerts.
This has allowed the bank to enhance their AML detection capability and have reduced the time to identify new links from three months to three weeks.
While we can't afford to be complacent, it's encouraging to see the battle is gradually being won.