Whether it is personalised content on your social media feeds, instructing Alexa to change the song or using FaceID to gain access to encrypted details on your smart phone, Artificial Intelligence (AI) is something we can no longer ignore and in some cases, we can’t imagine living without it.
It is impossible to discuss the role of AI in financial services without highlighting that 2020 was hugely disrupted by COVID-19 and the ripple effect is expected to last for years. Financial Institutions digital transformation strategies previously defined for 2020 quickly unravelled, exposing the inefficiencies to react and respond quickly when the pandemic gripped the world at an unprecedented speed. The reality is AI is encountered by most people from morning until night.
There has been debate over the true definition of AI as the expectations on what is deemed as ‘real intelligence’ change so often. At a high-level, AI as a field can be described as any technique that enables machines to solve a task like how humans would.
It could be leveraging Machine Learning, which utilises algorithms to allow computers to learn from examples without needing to be explicitly programmed to make decisions; or Natural Language Processing, which is focused on generating meaning and intent from text in a readable, natural form, or Computer Vision, which is focused on extracting meaning and intent from visual elements including images and videos.
Accelerated digital transformation
The rise of fintech and new technologies over the last decade has been significant and this has impacted how customers engage with organisations and in turn has transformed the financial services landscape. Changing customer expectations, fierce competition, increasing regulatory pressures and the strain to improve operational efficiency has seen the industry force itself into a reactive process where speed to market became even more vital for survival.. A new era of open banking has enabled systems to quickly and seamlessly integrate with new platforms and applications. Physical banks and paper systems are quickly being outdated and replaced by robust digital ecosystems, evident by the increasing emergence of new digital only challenger banks.
Digital transformation put simply is to rethink what we already create based on new technologies available. It is the process of modernising what we have done before. A digital transformation strategy must tailor an organisation’s response to crises, changing customer behaviour, and broader market conditions. It is here that AI can truly be leveraged.
AI excellence in financial services
Financial organisations are investing huge amounts of capital in digital capabilities such as chatbots, artificial intelligence (AI) and open APIs. The main advances over the past sixty years have been advances in search algorithms, machine learning algorithms, and integrating statistical analysis into understanding the world at large. The positive impacts that AI is having on financial services is growing.
- The use of AI in credit decision-making has become increasingly commonplace, with the potential to make quicker more accurate credit decisions based on an expanded set of available data. AI-assisted underwriting provides a 360-degree view of an applicant. It draws together big and traditional data; social, business and internet data; and unstructured data.
- AI is playing crucial role in fraud prevention by helping to analyse customer behaviour to anticipate or identify fraudulent purchases. Using a machine learning-based fraud detection solution could be trained to detect fraud within more than one type of transaction or application, or both of these at the same time.
- Much of the talk about AI in banking has been about how technology can replace some functions currently performed by humans. However, AI could also help financial organisations serve their customers more effectively by giving them easier access to relevant information.
- It is thought around 50% of manual jobs could be automated. These roles generally include physical activities in highly predictable and structured environments, as well as data collection and data processing. Process automation is hugely beneficial for financial service customers as their account applications, including lending and saving, can be sped up drastically.
According to Goldman Sachs, machine learning and AI will enable £26 billion to £33 billion in annual "cost savings and new revenue opportunities" within the financial sector by 2025.
Barriers to adoption of AI in financial services
Many companies and sectors lag in AI adoption. Developing an AI strategy with clearly defined benefits, finding talent with the appropriate skill sets, overcoming functional silos that constrain end-to-end deployment, and lacking ownership and commitment to AI on the part of leaders are among the barriers to adoption most often cited by executives.
- Lacking a culture of innovation – stakeholders within organisations hold immense power in the success of AI projects. Many financial organisations have small risk appetites this is filtered through business leaders on the ground responsible for IT transformation activities. When it comes to talent, training and upskilling are key. But this shouldn’t be just focused on the technology teams. Business teams also need to be upskilled in the art of the possible when it comes to AI, along with some of the drawbacks and other considerations.
- Data infrastructure - financial services firms typically suffer as their data is often siloed across multiple technologies and teams, with analytical capabilities often focused on specific use cases. The need to standardise data and ensure data is accessible is critical.
- Data privacy and cyber security - the use of personal information are critical issues to address if AI is to realise its potential. The General Data Protection Regulation (GDPR), which introduced more stringent consent requirements for data collection, gives users the right to be forgotten and the right to object which is a positive step in the right direction. Cybersecurity and scams that could manipulate perpetrate large-scale fraud are also a concern.
- Scrutinised costs - Costs in AI projects are often scrutinised by finance and senior leaders as the initial ROI is low. AI capabilities are long-term strategic investments so higher returns would be expected further down the line.
AI presents technological opportunities like no other. Unleashed from the orbit of science fiction, this is a real-world technology that is ready to be implemented in any business – today.
The capabilities of AI technologies will continue to grow exponentially as vast data sets needed for training AI solutions become more accessible. The time to move on AI is now. Low barriers to entry will bring ever fiercer competition for AI talent, AI patents and AI capabilities.
AI adopted early will transform the way financial institutions organise, run, accelerate and achieve growth. By implementing new innovative technologies, financial organisations will endeavour to reduce costs and create better experiences for customers and employees alike. This requires organisations to completely rethink their overall business operations including their workforce, a cultural shift is required to embrace new ways of working and technologies.
The uses and capabilities of AI continue to grow and change every day. This article highlights essential factors and advantages to be considered and further exploration is encouraged. AI should not be thought of as a business tool or extension of technology but rather as a transformative cultural change that needs to be considered in a very broad, multi-dimensional context.
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