Lloyds banks on technology in 2020 strategy
Lloyds Bank has set an ambitious three-year strategy to “digitise” the group. In its strategic review for 2018-20, Lloyds intends to use technology like data analytics and Artificial Intelligence (AI) to gain new insights which can transform key parts of its business. This is just the latest indication that financial services firms believe their future success rests on big data and AI.
In its strategic review for 2018-2020, Lloyds has pledged to make a strategic investment of more than £3 billion, of which more than half has been allocated to “digitising the group”. It now defines its business model as “digitised, simple, low risk [and] customer-focused.” A key priority is “simplification and progressive modernisation through targeted investment in technology, data, and innovation.” To support this change, it will to increase training and development by 50 percent by 2020. This training will include improving capabilities around data and applied sciences. It also aims to double its number of digital experience designers and engineers who specialise in robotics and AI. The bank predicts this strategy will have clear outcomes, including a positive effect on its bottom line. It anticipates that it will make efficiency improvements of up to 30 percent by 2020. The aim of the strategy is ambitious: it wants to be recognised as the best bank for customers, colleagues and shareholders, and to “help Britain prosper.”
In a 68-page presentation on the strategy which was given to analysts and investors, Lloyds spelled out the myriad ways it aims to exploit new opportunities enabled by technology. The bank hopes to use data insights to improve customers’ experience of digital banking. Voice biometrics will reduce the time it takes for customers to verify who they are over the telephone. With intelligent automation, Lloyds expects to reduce manual compliance efforts by 20 percent by using automated speech to text and analytics. Automated voice and chat-bots will improve the capacity of telephone banking staff by a third. By using cognitive and machine learning and building an enterprise data hub, Lloyds plans to enhance business intelligence across the organisation. Automating processes, connecting cross-group and external data and using API architecture and applied sciences for sophisticated analytics will also improve the capacity of its relationship managers. When these individual innovations are added up, they demonstrate a clear commitment to digitising the entire banking group and embedding technology in all aspects of the business.
Banking on data-driven insights
Recent announcements from rival banks suggest that data and AI technology are becoming the key weapons in financial services firms’ armoury. Earlier this year, HSBC announced that it would recruit 1,000 data scientists to grow its digital strategy. This month, Deutsche Bank launched a new enterprise analytics capability which collates and analyses millions of lines of data on securities transactions to identify opportunities for the bank and its clients. In a presentation to investors on 16 November, the Netherlands-based bank ABN AMRO said that innovation and technology would be “a critical enabler for efficiency.” A study by SNS Telecom & IT predicts financial services firms will invest $9 billion in big data this year, rising to $14 billion in 2021.
But big data’s potential is not limited to the financial services sector. Academics are using big data to analyse millions of research papers to inform their own research. The manufacturing sector is using Robotic Process Automation (RPA) to automate functions which previously took up a large amount of staff time. Media outlets such as Xinhua and the Washington Post are using AI to generate news stories. Leaders in sectors like sales and marketing are also using AI-powered big data analytics to find new insights.
Data as a Service complements internal data sources
Companies have access to data about their own customers and operations. Lloyds, for example, has data on the transactions carried out by its tens of millions of customers. But this data by itself is not enough to provide insights which can be applied in its operations. Companies often benefit from external data sources—ranging from news and social commentary to company market and legal data—which can provide added context to their own data.