These are uncertain times, especially for those in the finance industry. The regulatory picture is changing rapidly; the fact the world’s top investment banks have been fined a remarkable $43 billion over the past few years suggests they’re having a hard time maintaining compliance. Regulations such as Dodd-Frank in the US, EMIR in the EU, plus other requirements such as Basel III and FACTA, are continuing to put pressure on financial services firms by requiring more intensive reporting and access to data, meet compliance requirements.
Today, many banks are exploring the potential of big data analytics to help solve this problem. Getting such a project off the ground isn’t easy, but the benefits it can deliver as far as regulation is concerned can’t be overlooked. Plus, when combined with other innovations such as automation and Artificial Intelligence (AI), it can even transform the banks’ entire compliance process – and users might even find it having an impact on other lines of business, too.
Data + data = data
The sheer size of a bank’s IT operation makes any big data implementation a challenge. Global organisations inevitably have a variety of different processes in place, all changing at different rates. The problem is compounded by the fact that banks face many other factors such as data overload and inadequate data management systems, all of which make regulatory reporting more difficult.
As banks continue to become more diverse and complicated, the rate at which data is produced is ever increasing. To get ahead, banks need to apply big data processing and reporting tools to ingest, combine and process data, from both new and legacy sources. This then feeds a variety of application and analytical models, which ultimately helps to make the compliance process easier by providing clearer insights for compliance teams to work from.
For example, where compliance teams would previously rely on spreadsheet sampling to draw their conclusions, assets can now be automatically processed in an instant using analytics technology, cross-checking all relevant factors at once. This makes the auditor’s job easier too, as the process for recording transactions becomes far more efficient. This is why it’s crucial that these platforms are sophisticated enough to be able to process all types of data, whether that’s internal documentation, voicemails, customer correspondence or transactions.
Establishing this type of data analytics platform allows banks to reduce the complexity of their data processes and improve the speed of their analytical cycles. This not only lowers the cost of data processing, but it also enables banks to discover new insights in the data, allowing them to identify and manage risks more proactively. Best of all, this type of process can also help banks drive new insights, such as fraud patterns that may previously have gone unnoticed.
Automating the compliance process
The possibilities become even more exciting when combined with AI, which can hugely simplify various processes and reduce the chance of human error. AI will also continually self-improve, and can learn to work more efficiently – so once a process has been completed 50 times, the technology will be able to manage complicated, time consuming data updates better than its human users could ever do. This means that banks are better equipped banks to deal with the demands of regulators in a manner that just wouldn’t have been possible via a basic analytics tool or human intervention.
Beyond compliance, these technologies can benefit other lines of business, too. Big data offers banks an opportunity to differentiate themselves from the competition. Using advanced big data techniques to collect, process and analyse customer data, it helps personalise the services banks offer, making them more customer-centric. Personalisation of services is a significant factor in attracting and retaining customers in a commoditised banking market. By using big data analytics, banks can ensure they have as much data available on customers as possible, and thus offer them a tailored product or service at the right time. Examples could include, offering travel insurance after a transaction has been made to pay for a holiday, or offering preferential car finance when a current lease agreement has ended.
More effective use of customer data means that banks can deliver targeted and cost-effective marketing campaigns, design products and offers that are specifically tailored to customer needs, and even develop more accurate models for assessing creditworthiness and detecting transaction fraud. Even better, combining data sets in creative ways can really surprise and delight customers, leading to retention, loyalty and a higher customer lifetime value. Given the cost of customer churn, it’s time that banks take full advantage of these technologies to save hundreds of millions of dollars.
The article was originally published on Financial IT and is re-posted here by permission.