Financial services and technology vendors make for uneasy bedfellows. While tech has formed banking's bedrock since the Big Bang deregulation of the 1980s, in the last decade financial services (FS) organisations have seen the new "masters of the universe" steadily – almost stealthily – encroach on their patch. Established tech vendors and new start-ups have introduced a range of financial services from money transfer apps to mobile payments, crowdfunding to share trading and investments. These new services are perfectly suited to a generation who have grown up with smartphones and expect instant access to digital services, combined simplicity and a great user experience. While over the last few years there has been an exponential increase in the structured data that is collected and used, the inclusion of unstructured data sets, pictures, images and videos along with structured data has been increasingly important in driving both strategic and operational business decisions.

"Customer experience is chief among the areas where traditional financial institutions have fallen short. Not long ago, wealth management was considered a service almost exclusively confined to the affluent. With their millions at the ready, wealthy investors could use wealth managers to provide a range of tailored investment-related services, and those services would normally come at a high price. But these days, such a perception of wealth management is becoming old, or simply not accurate. Innovation broke down those barriers of exclusivity, enabling services that were previously only accessible to the privileged few to be in the hands of the masses of ordinary investors. Wealthtech falls under fintech as a segment which specifically focuses on technology that aims to transform wealth management and retail investment. It involves the application of digital solutions to wealth management, ultimately providing new channels to deliver more efficient, cost-effective and efficient ...

GlobalData has revealed that the number of gig workers has increased in the world's largest economies, prompting lender curiosity about expanding their access to credit. Gig workers who previously struggled in the lending ecosystem due to their temporary, freelance jobs wanting steady pay cheques could now utilise artificial intelligence (AI) to convert them into creditworthy borrowers, by using alternative data. Kiran Raj, Principal Disruptive Tech Analyst at GlobalData, noted that traditional credit scoring models, such as FICO, are inherently flawed in accessing thin credit files due to their assessment of only a handful of standard data variables. Raj acknowledged that this leads to lenders reeling under pressure to make more inclusive credit decisions in real time. "This is where fintech start-ups have come into action with their AI credit scoring models almost instantly interpreting alternative data like historical payments, digital footprint and behavioural economics," Raj said.

Financial services were one of the first sectors to understand the promise of the Big Data revolution, and the wave of new technology which has come with it – including artificial intelligence (AI). This isn't surprising – businesses in the sector traditionally define themselves by their ability to interpret and analyse structured data, and use it for making predictions and decisions. The shift towards Big Data has meant applying what they know about working with structured data – data which fits neatly into the rows and columns of a spreadsheet – to working with the messy, unstructured data that we are generating today, due to the increasingly digital, connected and online world we live in. Always keen to develop and exploit a new competitive edge, in recent years the financial sector has put the latest technology to work driving operational changes, increasing rates of fraud detection, improving customer services and developing new products. However, that's not to say it doesn't bring problems of its own!

What will be the top technology priorities and trends in the banking industry in 2017? New technology innovations and shifting competitive landscape due to the entry of fintech start-ups, payment players and challenger banks will change how transactions will be carried out in the future. All of these changes are forcing banks to relook at their technology capabilities and operating and service delivery models to retain wallet share. While technology transformation is inevitable (and will probably be rapid), few areas emerge as distinct priorities of financial institutions and hot trends for 2017. Technology priorities of banks in 2017 A survey across some of the leading banks in Asia Pacific on their key priorities in technology investment in 2017 highlights following focus areas (Figure 1). Improving mobile and digital experience remains a key priority for financial institutions in 2017.

Despite the enormous changes in recent years, including the emergence of the plethora of significant new market players – including fintech start-ups, established payment, technology, and information firms, telecoms, and other providers, the financial services market is indeed in the middle of a revolution and this is down to both technology and regulations. This begs the question of how financial services incumbents will fare is far from settled and offers a scenario wherein these incumbents looking to grow shareholder value will need to build and sustain new competitive advantages. The European Commission's revised Payment Service Directive (PSD2) represents a broad sweep of financial services sector regulations that will come into force next year. In summary, PSD2 creates the opportunity for digital actors to link directly into payment systems via API's. The regulation will require that banks provide these API's so that third-party service providers will be able to directly access customers' accounts.

AI in Production – AI is only used by banks in production in a few key use cases such as high-frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

AI in Production – AI is only used by banks in production in a few key use cases such as high frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalising models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

LONDON--(BUSINESS WIRE)--According to the latest research study released by Technavio, the global FinTech investment market is expected to grow at a CAGR of over 53% until 2020. This research report titled'Global FinTech Investment Market 2016-2020', provides an in-depth analysis of market growth in terms of revenue and emerging market trends. This market research report also includes up to date analysis and forecasts for various market segments and all leading regions. "FinTech companies seek new means to store, analyze, and search vast amounts of data. Such analysis is anticipated to help them segment customer populations, identify opportunities for new products and services, and optimize pricing mechanisms. A key example is this is seen with the pooling of social network data with fund management and investments in relation to company analysis and management. The use of big data and new data can improve investment decisions, and also help arrive at a comprehensive credit scoring mechanisms," said Soumya Mutsuddi, one of Technavio's lead research analysts for gaming.