British banks are set to ramp up digital tools and customer support teams to cope with a wave of consumers and business owners looking for help in the year ahead. Lenders will have a vital role in providing support as household incomes are further squeezed, mortgage rates rise, and businesses struggle, experts have said. Krishnapriya Banerjee, a managing director in Accenture's UK banking practice, told the PA news agency that banks are being challenged to improve how they engage with customers amid looming recession fears. She told PA: "There is a bigger focus on providing the right service to the consumer, whether that's the contact centres and dealing with the flurry of calls that come through, or using a higher amount of data, artificial intelligence, or conversational technology to support those conversations. "I think we will see much greater utilisation of technology to support customer interaction in the year ahead." For example, companies can now use voice recognition technology to detect emotional distress among customers, which Ms Banerjee said could be the key for lenders to proactively prevent forbearance calls from borrowers at risk of defaulting on their loan payments at a later stage. She added: "One of the things that we are seeing with the cost-of-living crisis is that people are really thinking about how they are budgeting their money.

Silent Eight announced an extension to its existing partnership with HSBC to tackle financial crime. The new service will cover the deployment of Negative News Screening that leverages machine learning to identify individuals who pose a greater risk for money laundering, fraud or terrorist financing. As financial crime continues to present a challenge, banks need to pivot to an increased use of Machine Learning within compliance, and move away from manual processes or alert scoring. Silent Eights' solution provides a more effective approach to address true matches and resolve the issue of false identifications. With this expansion, Silent Eight will be solving name screening matches across every risk type within HSBC.

In the long shadow of the 2007-08 global financial crisis, concurrent advances in three technologies – smartphones, 4G cellular networks and cloud computing – sparked an explosion of innovation in financial services. Their convergence enabled mobile banking: the sector's most significant development in generations. Just over a decade later, the industry is again "on the cusp of another inflexion point". He predicts that progress in tech including 5G, blockchain, artificial intelligence and quantum computing will trigger "another spurt" of innovation. "People talk about data being the new oil," Pattni says.

In my last blog, I shared some thoughts on how different forms of AI could help transform financial services, along with some challenges that needed to be overcome to ensure successful enterprise wide adoption. As promised, this blog will focus on how AI can help improve the customer experience in banking; both directly, and more so indirectly behind the scenes. When it comes to thinking about how AI can be applied to improve the customer experience in banking, many people will immediately jump to images of using "robots" to serve customers; these could be actual robots (such as HSBC's employment of "Pepper" in downtown Manhattan), or virtual agents (be it chatbots, robo-advisors etc) to service customer requests either on the phone or via chat tools. Whilst HSBC saw some success with "Pepper", it is safe to say that the more wide spread adoption of AI for customer service is focused on virtual agents. Whilst virtual agents aren't new (I remember helping a bank roll them out at least 10 years ago), their functionality has moved on massively from the heavily scripted solutions of a decade ago, to capabilities that utilise "Conversational AI" which help make the virtual agent interact with humans in a more human like way.

HSBC is one of the world's largest financial institutions, serving more than 40 million customers globally. One of its largest divisions, Wealth and Personal Banking, supports individuals, families, business owners, investors and entrepreneurs. It provides products and services that include current accounts, credit cards, personal loans and mortgages, as well as savings, investments, insurance and wealth management. At the centre of the Wealth and Personal Banking division is a data analytics group, which is responsible for providing data-tailored services to HSBC teams and customers all around the world. Rahul Boteju, Global Head of Data Analytics at HSBC, was speaking this week at the Big Data LDN event, where he shed some light on what it takes to build an effective data science team that can scale.

MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.

HSBC has built an automated, AI-based algorithm (in partnership with EquBot and IBM Watson) to read millions of data points about 1,000 of the largest publicly traded US companies. Data fed to the algorithm includes everything from press releases and tweets to satellite images of parking lots and the tone and language used during earnings calls. Based on these inputs, HSBC's AI algorithm automatically selects companies that it expects will see a rising stock price in coming months. These companies are organized into a fund--AiPEX--that is rebalanced at the end of each month. The algorithm also accounts for market volatility by modifying the balance of stocks and cash in the AiPEX fund on a daily basis.

When a survey in 2015 revealed that more than half of Guangzhou's female commuters had experienced some form of sexual harassment ("inappropriate touching") on public transport, a handful of Chinese cities began reserving subway cars for female commuters. But the designated carriages, which were sometimes labelled in pink Chinese characters with floral adornments, did little to deter men from squeezing aboard. "When everyone is rushing to work, no one cares whether it is a female-only car or not," one commuter complained on weibo. Indeed, many men have either blatantly ignored the restrictions or were oblivious to women-only subway carriages. Enforcement has lacked teeth – in part because the metro system is so overcrowded.

Niccolo Mejia covers AI applications across industries at Emerj. He holds a bachelor's degree in Writing, Literature, and Publishing from Emerson College. HSBC Holdings is a multinational banking and financial services holding company and is ranked 99th on the Fortune 500 list. The bank has worked with multiple AI vendors and provided evidence of success that other banks may be able to study and take advantage of. In this article, we cover the bank's work with two vendors in particular: We begin our dive into HSBC's AI initiatives with its anti-money laundering solution from Ayasdi.

MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.