Contact centres for financial institutions have traditionally been a core touch point for customers to access various types of immediate support – from queries to complaints to fraud alerting. Today their role hasn't necessarily changed. However, the value organisations place on them certainly has. The focus is shifting from fitting customers around business processes to reshaping contact centres around customers' needs. For years, the role of contact centres was limited – often confined by traditional 9-5 working hours. It was predominantly aimed at driving down costs and improving efficiencies.

Customer experience is one of the pillars upon which business success is founded. The quality of the journey a customer makes when engaging with a company determines their ongoing loyalty. For financial organisations, which handle people's income and monetary assets, the entire customer engagement process must be as stress-free as possible. The two main priorities for financial services contact centres is first-contact resolution and ensuring quick response times. Put it this way, if you call your bank and are passed off onto multiple agents – each one knowing as little as the last – before you get your response, how confident would you feel in the company's customer service?

When these events occur, decision-makers within financial organisations need to analyse every scenario and act quickly – using data-extracted insights to guide the way. In this piece, InterSystems' Director of Product Management Jeff Fried explains how machine learning can play an integral role in building resilience within the financial sector, using real-time examples such as automated trading, fraud detection, and customer experience (CX) initiatives that utilise modern data management and analytics capabilities to make it through the most strenuous business challenges with almost no effect on the operations of the organisation itself. Before the pandemic, increasing business agility was of utmost importance for organisations across industries. But what was once a battle of speed and first-to-market has now expanded its focus to include business resilience. Recent events have underscored the need to weather the storm during volatile operational or security-related situations.

Of course, there is no shortage of data in financial services -- structured, unstructured, transactional, account-level – but while this data brings benefits, when in the hands of nefarious actors, also make fraud more pervasive. Neil Murphy is the Global VP at ABBYY, and believes AI is the way forward to tackle the ever rising cases of fraud. Along with other benefits, Murphy explains by using AI, financial organisations can reduce manual steps required in the onboarding stage, and process both structured and unstructured documents. In doing so, financial organisations gain a birds-eye view and can filter out suspicious and fraudulent actors. Technological advancements, an increase in investment into security systems and fraud prevention initiatives have been widely adopted by the finance industry in an effort to curb scams and crises from occurring.

The ones old enough to remember, might recall the times when image communication seemed a far distant impossible reality… a futuristic dream that would never be achieved! In the last few decades, we have seen what seems like reality surpassing imagination. Incredible technological advancements are happening at such quick pace that one is almost incapable to keep up with all the innovations. And one of the most radical inventions that is promising a tsunami in our lives is AI. The tsunami AI, has been out there actually for a long time.

Data Analytics has been the backbone of some of the revolutionary companies that are disrupting the ecosystem. Financial Services is no different. These challenges are picked by our editorial team based on the latest trends and development. Do read it till the end to enhance your understanding of the subject. If you think we are missing anything, let us know in the comments and our team will review and add it into the blog.

Up until recently, compliance has mainly relied on people. And as a result of the significant increase in regulatory reporting requirements for financial institutions over the last decade, demand for compliance professionals has surged. Companies have had no choice but to hire more and more compliance staff, in an effort to tackle the growing regulatory burden. However, over the last few years, technology has begun to play a much larger role within compliance. Financial institutions and regulators have realised that by harnessing the power of technology, and more specifically, the power of artificial intelligence (AI) and machine learning (ML), a considerable proportion of the compliance function can actually be automated, reducing the burden on institutions and compliance professionals.

There is huge amount of noise currently about the use of artificial intelligence (AI) in the financial services sector. Every fintech application or new piece of banking software must be accompanied by bold claims about its use of AI, even though in many cases they are simply upgraded algorithms. It would certainly be beneficial if we had an AI application that could cut through the spin and present us with only what is relevant to the specific requirements of our job or organisation. In truth, such advanced filtering applications are already available. They are just one of the AI-driven applications that are going to transform the way customers view financial organisations, especially in the retail banking and lending sector. See also: Should financial experts fear the rise of artificial intelligence?

The financial services industry has witnessed considerable hype around artificial intelligence (AI) in recent months. AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the'holy grail of the data age.' Devising an AI strategy has therefore become a boardroom conundrum for many business leaders. How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies?

The financial services industry has witnessed considerable hype around Artificial Intelligence (AI) in recent months. AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the'holy grail of the data age.' Devising an AI strategy has therefore become a boardroom conundrum for many business leaders. How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies?