Artificial Intelligence (AI) can be categorized into three application domains from a banking and financial services perspective:
Cognitive Automation: Cognitive tools to develop deep-domain specific expertise and automate relevant tasks
Machine Learning: Analysis of customer activities, NLP and recommendation algorithms to provide insights to customer, drive engagement, offer new products and open up new revenue streams
Cognitive Computing: Extraction of concepts and relationships from various data streams to detect data patterns and relationships to derive insights which can be converted to action items
Financial organizations have a greater opportunity to leverage artificial intelligence as they have access to a huge amount of data for artificial intelligence algorithms. Also, consumers are willing to share personal insights if they can get greater value in return. And most banks and financial organizations are already utilizing some form of analytical tools, so relevant algorithms and processes are also well-defined and standardized.
Using the components of artificial intelligence, there are several use cases in the banking domain where AI can be applied:
Cognitive Computing: Algorithmic trading and automated investment management
Machine Learning: Next best action and next best offers, wealth management robotic advisors, fraud detection and anti-money laundering
Natural Language Processing: Chatbots, robo-advisors
The diversity of the use cases indicate that AI has the potential to impact multiple touchpoints of the banking system ranging from adherence to compliance to greater customer engagement. In terms of customer engagement there is potential to democratize activities like wealth management advisory which till date was available for only a select group of banking clients. Also, banks can have greater insights about customers, which can lead to faster decision making and better levels of engagement. This will help them offer new personalized products thus opening up new value streams.
Besides enhanced customer engagement, AI also brings along the opportunity of automating procedural and repetitive tasks. And with ample amount of data, it can also contribute to automation of support activities like performing root-cause analysis, defect analysis, automated query resolution etc. This enables support staff to concentrate on complex queries thereby reducing turn-around time, improving efficiency as well as enabling time and cost optimization.
Hence we can see that AI and its applications have the potential to impact the ROI for a financial organization in a two-pronged manner:
They influence both the revenue generation, and expenses of a bank/financial institution. Diligent and effective implementation of AI use cases can lead to greater revenue generation as well as expense reduction. The contribution of AI on banks’ ROI is expected to go up further as more data and related insights become available and new AI use cases come up.