5 uses for AI in financial services
Why should AI be used in finance?
In the world of finance, even small errors can cause significant results. You need only look to Santandar, which paid out £130 million on Christmas Day 2021 in error due to a scheduling issue that resulted in roughly 75,000 accounts being paid a second time from the bank’s own reserves.
Intelligent Automation (IA), powered by Artificial Intelligence (AI), offers a powerful asset for handling complex, data-driven tasks, making it perfectly suited for the financial industry. It’s estimated that there are over 1 billion purchase transactions around the world every day, all relying on technology infrastructure to do two essential tasks – transfer money and, most importantly, do it quickly. How, then, can AI help financial institutions?
Use #1: Achieve growth without compromising older systems
Financial companies are often long-established and have made many changes in their systems over time. In some cases, these systems have become monolithic and are essential to the mission-critical operations of the organisation. Downtime of these systems, either planned or unplanned, risks consumer irritation and can detrimentally impact confidence in an institutional brand. Due to the often global customer base that relies on these systems, financial companies can feel torn between growing their capabilities and putting older code at risk. This has given rise to leaner, challenger banks who have dominated consumer score tables in recent years.
Intelligent Automation provides a strategic solution for introducing the benefits of a modernisation programme while maintaining the core systems and architecture that the organisation runs on. Where legacy systems lack modern APIs or clean back-end processes, AI-driven solutions can interact with the front end of existing internal systems, mimicking human actions to execute complex tasks. This allows an organisation to achieve performance indicators previously thought to be years away in a fraction of the time. It also provides IT teams with the necessary breathing room to continue with long-term modernisation, but with reduced risk and time-sensitive pressures.
Use #2: Maintain regulatory compliance
Another byproduct of the size of many financial companies is the volume of regulations and guidelines they must remain compliant with to continue operating in their many international markets. Compliance is a notoriously laborious task, though essential to business operations. It requires analysis free of bias, making it a prime candidate for AI and Intelligent Automation.
AI models can be trained to understand regulatory documents, identify changes, and assess their impact on internal processes. Advanced logging can be applied to any automated process to confirm with, or expand on, the required audit trail. This can be achieved by introducing business-readable checkpoints through emails, scheduled reports, or long-term activity logs, which can be made accessible at any time should an audit be required. AI systems will not deviate from their programmed logic, nor will they make mistakes, assuming they have been developed to a high quality. As such, you can be confident that your intelligent systems are diligently working to ensure processes remain accurate, compliant, and auditable within specified timeframes.
Use #3: Increased efficiency
Intelligent Automation is an exceptional tool for removing inefficiencies in people’s workflows. This can have a massive impact in the world of finance as companies regularly have many departments and carry large headcounts. By automating data-intensive and repetitive processes, AI frees up human intellect for more strategic work.
Utilising this technology, regular tasks such as running audits, analysing transaction patterns for fraud, and generating system reports can be carried out by an intelligent system, overseen by a small number of human supervisors. Staff time can therefore be redeployed away from inefficient workflows. This means everyone from senior staff to more junior team members can focus on higher-value tasks where human insight, creativity, and empathy are required.
Use #4: Burst capability
A significant advantage of investing in an intelligent, automated workforce is the comparatively low cost for each hour of productivity. For example, an intelligent automation licence can operate 24/7, delivering substantial value for a predictable cost.
A properly configured intelligent automation platform would not be restricted to any specific task or function. Instead, AI-powered systems can operate across an organisation, focusing on tasks according to a multitude of specified business logic such as job priority, SLA, or completion time targets. This allows the organisation to automatically reallocate and scale its digital resources according to the greatest need of the business at short notice. This capability can be deployed with no additional training requirements or configuration, whilst de-prioritising tasks of lower importance until the burst in demand is over.
Use #5: Improved customer experience
Many financial companies already have two features in place to carry out the bulk of their customer service work: call centres and chatbots. However, there is great potential for AI to enhance how these two functions work.
AI can dramatically improve chatbot functionality, moving beyond simple, scripted answers to understanding customer intent and resolving complex queries. This allows customers to complete a greater number of tasks by themselves and with 24/7 access. Human supervisors are still needed for issue resolution and to complete more sophisticated tasks that cannot be completed by an automated process. However, AI can assist here too. By using natural language processing (NLP) to monitor call centre conversations in real-time, AI can provide agents with relevant information and next-best-action suggestions, speeding up tasks that require a supervisor and improving first-call resolution.