Blockchain, AI, predictive analytics, APIs and biometrics – five disruptive technologies which can better detect and prevent financial crime.
Customers expect digital services that fit in with their everyday lives. To stay in step with consumer demands, financial institutions are continually expanding their offerings to deliver the speed, ease and convenience customers expect. While this creates a more positive customer experience, it can also open up new avenues for attack.
Fortunately, advancements in technology can help to mitigate the threat of fraud in real time and enable financial institutions to better detect and prevent financial crime while also meeting regulatory requirements. Here we look at five disruptive technologies that can do just that.
1. Artificial Intelligence and Machine Learning
Artificial intelligence (AI) is rooted in computer systems performing tasks that normally require human intelligence, such as visual perception, speech recognition and decision making. Machine learning is a subset of AI in which systems learn when exposed to new data rather than being programmed to perform a specific function. AI technologies have been at work in financial services for some time and are rapidly evolving the customer experience and the back office.
One aspect of machine learning involves identifying patterns of behaviour from large data sources. This is vital for the detection and prevention of financial crime. Through the continuous monitoring of comprehensive data, customer behaviour patterns can be identified to establish when a particular behaviour is out of the ordinary. Machine learning can take behavioural analysis to the individual consumer level, improving detection and reducing false-positive rates, while facilitating regulatory compliance.
Data analysis from machine learning can be used by financial institutions to detect potential fraud and money laundering in real time and more accurately. Real instances of financial crime can then be investigated immediately, while customers who are carrying out legitimate transactions can do so without disruption.
Blockchain lets participants transfer ownership of digital assets and then records those transactions on a ledger in real, or near real time. All parties are recorded, all information is accessible to those with access to the ledger, and the ledger enables real-time validation. This high level of security makes it much more difficult to carry out financial crimes.
Many organisations are considering how Blockchain technology can reform their current processes and procedures around authentication, data integrity and security. As financial service organisations are often the record-keepers for transactions such as security trades, loans and payments, the implications of Blockchain are far-reaching.
Biometrics – the measurement and analysis of unique physical or behavioural characteristics, especially as a means of verifying personal identity – is both old and new. Using fingerprint ID to solve crimes has been around since the late 1800s. In the past few years however, advancements of biometrics have skyrocketed, with some capabilities such as unlocking smartphones and tablets with a fingerprint or via facial recognition becoming more commonplace.
Some applications of the technology in financial services include biometric ID used by mobile banking applications, credit card “selfie” facial recognition and palm vein authentication. The use of biometrics for authentication is an unobtrusive way to enhance security. By presenting information unique to an individual in a quick, consistent and accurate way, a genuine customer can be identified before the financial transaction gets underway. Biometric authentication relies on a mathematical representation of physical attributes that would be very challenging to re-create to gain unauthorised access.
As biometrics becomes more commonplace in mainstream banking, it presents an opportunity for multi-layered fraud mitigation approaches. We could see mobile device facial recognition policies for high-value wire transfers, or combinations of biometrics, such as requiring both a voice print and facial recognition when making transactions. Biometrics could even replace passwords; iPhone X with FaceID has done just that.
4. Predictive Analytics
Predictive analytics turns data into valuable, actionable information used to determine the probable outcome of an event or the likelihood of a situation occurring. While predictive analytics is in use today, it is also evolving. Predictive models must be trained on new fraud and money laundering techniques if they are to provide financial institutions with the ability to rapidly respond to new attacks.
A hybrid analytics model often analyses particular customer and transaction information from the financial institution against broader data sets. While data on its own can be useful, by reviewing this against other sources across the industry, the actionable information from that data becomes richer and patterns can be identified. These patterns can then be used to predict, detect and prevent fraudulent behaviour.
In addition, hybrid analytics models are purpose-built to let the business experts within risk departments influence the model. Future applications could create super-hybrid models in which individual financial institutions could link to country-level or global-level information in a secure cloud environment. Hybrid analytics could also be combined with machine learning and other advanced technologies to provide even more accurate financial crime detection and prevention.
5. Application Programming Interface (API)
APIs have been used by IT teams for years to connect applications for the purpose of sharing data and functionality. APIs make integration faster, less costly and easier.
Financial institutions can leverage APIs to drive risk-based financial crime strategies that allow for nimble reactions to crime scenarios. The ability to pull in particular customer and third-party data enables financial organisations to snap in – and snap out – solutions for different business needs to balance financial crime mitigation, client experience and operational costs.
Technology that transforms
Financial institutions strive to deliver the experience their customers expect. However, they must balance customer experience with the need for security and regulatory compliance. In order to do this, institutions can leverage new technology trends that enhance the customer experience while also enhancing security, avoiding many of the traditional trade-offs in the areas. Using innovative anti-crime technologies means financial institutions can provide positive customer interactions while staying one step ahead of criminals.