Innovation and changing consumer behaviour will determine the most important online trends for the year As we move into a new decade, technology is continuing to make big changes in the way eCommerce businesses engage with their customers. Creating a satisfying customer experience (CX) is vital for any eCommerce business to be successful. AI powered chatbots are becoming increasingly popular for the way they can quickly interact with customers by providing fast, real-time support regardless of time or location. A well-designed AI powered chatbot can learn from customer engagements to improve the accuracy of its responses. These smart interactions can significantly reduce response time and help to maintain customer satisfaction.

Technology has taken centre stage in the success of companies today. With the likes of Uber, Amazon, and Deliveroo changing the way we live, shop, work and consume content, innovation is happening faster than ever before. In light of economic uncertainty, it's become even more vital for businesses to deploy cutting-edge technology to maintain competitiveness. Over the course of the next year, board-level conversations will be dominated by ways to ensure a seamless customer experience, formulating tactics to embrace disruptive technologies, as well as grappling with the implications of the future workplace. Consumers can now order a meal, book a taxi and do their shopping with a few clicks of a button, without even leaving their living rooms.

Across the globe, companies are amassing volumes of data with the intent of optimising performance, identifying trends and meeting rising consumer expectations. Yet nearly 75% of global financial services and insurance executives admit they are challenged by the fractured nature and vast amount of data available, citing rich analytics capabilities as difficult to achieve. In the UK alone, 71% of executives admit they are challenged by the immense data they have. With these challenges in mind, a new Aite Group study commissioned by TransUnion found that executives in the financial services and insurance industries plan on continuing to secure more data sources. Furthermore, they look to incorporate more artificial intelligence (AI) and machine learning (ML) technology into their analytic platforms to help them make sense of the information.

Neotas – a leading provider of online due diligence for the financial services sector – is joining forces with artificial intelligence (AI) experts at the University of Essex to develop a next-generation screening system. The London-based company has been awarded a £200,000 grant from Innovate UK towards the first stage of the project, which it believes could revolutionise due diligence by providing new solutions to tackle financial crime and mitigate risk. Neotas uses powerful search techniques to analyse a company's or an individual's'digital footprint', providing advanced insights without invading their privacy. Clients range from private equity firms carrying out pre-investment checks to banks and institutions which need to comply with anti-money laundering (AML) rules or the Senior Managers & Certification Regime (SMCR). The project aims to make the screening process less labour intensive and automatically identify warning signs without creating false alarms.

As the technology evolution has progressed, have we all lost our jobs?

The banking sector has witnessed multiple paradigm shifts, traditionally occurring in parallel with emergent technology trends. Technologies that were previously at the forefront of banking transformations are now standard issue by modern banks, with the most notable examples taken for granted by millions of customers on a daily basis. The advent of credit cards and magnetic strip technology arrived during the 1960's and reached mass adoption during the 1970's. At the same time, the first ATM was installed in 1967 in London, easing customers' access to their funds, and simplifying back office processes. However, the most major transformation to date was the introduction of online banking.

Financial institutions are learning the hard way what can happen when criminals get their hands on the latest technology. Gone are the days of robbing tellers at gunpoint; today's sophisticated criminal networks and nation-states use machine learning and artificial intelligence to target institutions remotely, quietly and through many channels at once. They have figured out banks' vulnerabilities and exploited them mercilessly through a combination of malware, ATM jackpotting, money mules, money laundering, e-payment/cryptocurrency fraud and more, stealing untold millions to finance terrorism and profit from human and drug trafficking. So, what exactly is "cross-channel fraud"? A perfect example can be seen in the tremendously successful Carbanak campaign, which used cross-channel methods to steal more than a billion euros from over 100 banks in 40 countries.

The use of machine learning in finance can clearly do wonders, it's a technology that is such a great fit for the financial services industry. So let's take a closer look at how companies can utilise it. As a subset of data science, machine learning uses specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more. Plus, you can regularly update the mathematical models, so they can effectively learn from both experience and new data. So there is a clear fit with the quantitative nature of the financial services industry.