Taking an AI approach to finance fraud
Fraud is a million-pound problem with one in fifteen people falling victim every year, making it one of the most common crimes in the UK. To combat financial crime, the industry has been investing heavily in its security system, fraud prevention initiatives and technology. However, the financial criminals are continuously adapting their methods and the battle against fraud rages on. In fact, according to UK Finance, unauthorised financial fraud losses across payment cards, remote banking and cheques totalled almost £800 million in 2020. It should come as no surprise then that the way financial organisations assess and manage risk needs to change.
Technology can be a double-edged sword when it comes to mitigating financial fraud. When technology evolves, criminals use increasingly sophisticated methods to carry out attacks. Therefore, organisations must take a holistic approach to protecting themselves by taking a closer look at improving their processes, using data more effectively and harnessing a combination of artificial intelligence (AI) technologies to be safe and secure. Only by wielding these weapons can businesses win the battle against financial crime.
In an effort to combat financial fraud, businesses must take a holistic, AI powered approach – but what does this involve?
Turning data insight to impact
Financial services are a data heavy industry. From structured, unstructured, transactional and account-level, it’s clear that there is no shortage of it. While this data brings benefits in terms of consumer insights, in the hands of nefarious actors, it can make fraud more pervasive. It’s true that initiatives like General Data Protection Regulation (GDPR) have certainly ramped up the regulation of consumer data, but there is still a huge opportunity to uncover insights with the data to bring more benefits to the financial organisation.
In the finance industry, building trust and customer loyalty are incredibly important, which is why fraud can be detrimental to their reputation, and result in added cost. By collecting the data from fraud, anti-money laundering (AML), and cybersecurity, financial organisations can consolidate information across historically isolated functions and make more informed decisions with a holistic view of risk.
By identifying similarities from the data collected across AML, fraud, and cyber teams, breaking down these silos can provide a more transparent view of the threat landscape, better detect suspicious transactions, and streamline investigations. Since the criminals are using cyberspace to commit fraud and ultimately need to monetise that information so that the funds appear legitimate, it makes business sense to bring these functions together.
360-degree view to fraud prevention
Before financial organisations look toward technology to mitigate financial fraud, there are few steps they must take to ensure they are making the most out of these investments. First financial institutions need to be able to identify the bottlenecks and blind spots in their business processes, to see which areas need to be improved and where automation will be needed. With process intelligence organisations can analyse less structured processes, identify opportunities for improvement, and increase both the speed and accuracy of executing said processes. With this holistic approach, financial institutions can collect and analyse intelligence from across the organisation. This model improves intelligence sharing across the industry and allows financial institutions to continuously test and improve their security playbooks.
Once they have a 360-degree overview of their business processes, the most effective place to begin automating is the onboarding process. Streamlining onboarding by leveraging modern technologies enables financial institutions to filter out fraudulent actors and deliver a more frictionless experience for their customers. Using a combination of technologies, for example artificial intelligence (AI), robotic process automation (RPA), and natural language processing (NLP), can enable financial institutions to process both structured and unstructured documents, minimise manual steps, and reduce the need for making redundant requests of the client. Establishing an effective client onboarding process not only enables faster detection of potential fraud but plays a significant role in developing relationships with new clients.
One thing is for sure, a holistic strategy provides the visibility necessary to better prepare for auditing and compliance requirements. It improves efficiency, protects the brand and reputation, and protects against sanctions or fines. There is greater protection against identity theft and fraud from a customer perspective, and fewer security incidents increase uptime, allowing customers seamless access to their financial lives.
AI is powering the future of finance
Artificial intelligence has taken the world by storm and the financial services sector is no different. Investing in the right AI technologies can see the industry reap the benefits of a more secure and efficient organisation.
With AI-powered solutions for improving and monitoring business processes and intelligent document processing, financial organisations can reduce manual steps required in the onboarding stage and automate both structured and unstructured documents. This enables them to have a birds-eye view of customer data so that they can distinguish when activity is suspicious and fraudulent.
By opening up visibility and reducing manual effort aided by AI, RPA, and NLP, the frontline finance teams can focus on protection and mitigation and create robust fraud detection and prevention systems. Evidently, there are a whole host of benefits that come when a financial institution achieves a holistic fraud prevention strategy, free of limiting silos.
By taking new steps to fraud prevention, risk through this lens requires more than technology investment. It’s the combined focus on people, processes, and content in the successful implementation of a holistic approach.
Businesses must think strategically about AI and understand it’s impact to truly achieve fraud-free finance.