The answer to solving financial crime lies in data

Financial institutions are engaged in a constant battle to fight money laundering, bribery, fraud, terrorist financing and corruption and adhere to sanctions levied against countries and individuals. And financial crime takes a massive toll – it puts global financial systems at risk, it negatively impacts economic growth and causes massive losses to companies and people. It’s been estimated that annually, it costs the global economy as much as $2.1 trillion.

With the incidence of financial crime continuing to escalate, it’s vital the focus remains on putting the right solutions and processes in place to fight it. After all, anti-financial crime processes are not only necessary from a business and reputational perspective – they are a regulatory imperative. Financial institutions – and in some cases, individuals – can be held criminally accountable for not adhering to financial crime regulations.

The regulatory environment and the complexity of the financial system means that vast quantities of data are produced, but manual processes and legacy banking systems mean many organisations often struggle to cope with these large data volumes.

But the answer to mitigating the risks of financial crime lies in being able to manage and use this data in the right way. Data engineering, data science and cloud engineering all have an important role to play in optimising the way organisations use data.

With global spend on financial crime prevention expected to exceed $28bn by 2027, these are three vital components of a successful financial crime approach:

• Financial crime analytics platform: implementing a financial crime analytics platform increases the speed, agility and intelligence to help organisations combat financial crime. For example, a platform which automates the distribution of alerts and event driven reviews improves the efficiency and accuracy for fincrime teams.
• Machine learning: developments in machine learning (ML) have really helped banks get on the front foot with fighting financial crime. For example, the development of a centralised Machine Learning (ML) model repository can really enable improved decision making.
• Increasing automation of manual processes: improving the level of automation helps financial institutions process vast amounts of data much more quickly in a fully auditable manner. This frees up analysts to deal with actual financial crime threats.