One of the largest and most important trends facing the banking industry today is how best to apply Artificial Intelligence (AI) and advanced analytics to reshape every part of the business from internal operations to customer experience to treasury services and payments. More transactions are moving from manual to digital.
Structured and unstructured data is exploding both inside and outside the organization. Corporate customers expect more from their banking services. We’ll be discussing the role of AI enhanced analytics in the payment business at SIBOS this October so I thought it would be good to set the scene in a short blog.
Here’s a sobering prediction: Ray Kurzweil from Google estimates that AI will surpass human intelligence by 2019. While the earth is unlikely to be run by robots any time soon, this does mean that the service that customers receive from their bank, insurance firm, phone company and utilities provider should improve. AI has the potential to help us do many things better, faster, cheaper and with fewer errors than any manual process. Combined with advanced analytics, AI delivers insight into what customers are doing today and what they are likely to do tomorrow.
What is AI-enhanced analytics?
AI is hardly a new concept for the banking industry. It is already embedded within some key surveillance processes such as fraud detection and prevention. What is new is the amount and variety of data that must be analyzed, and the advances that have taken place in natural language processing technology, machine learning algorithms and expert systems (systems design to provide advice). AI is simply getting better and better at automating repetitive, transaction-intensive work processes. While predictive analytics has been used for business optimization for some time, the missing piece of the puzzle has been a cognitive approach that can take all sources of data, intelligently uncover trends and patterns, and use the insight to inform better decision-making.
As our CEO, Mark Barrenechea discussed with the Financial Post: “The big value-add is getting insight out of all that automation. You need to run payroll, but can you get insight about fraud? You need to automate payments, but can you get insight about risk management?”
AI-enhanced analytics adds the capability to derive insight from all that data and content, while at the same time filtering out ‘data noise’ from the valuable information. Advanced analytics allows organizations to leverage all the structured and unstructured information in their Enterprise Information Management systems. Algorithms and cognitive models help identify trends and patterns in the vast amounts of data and form coherent conclusions. Natural language processing and machine learning become more effective with higher volumes of data, meaning the more data that is analyzed, the better and more accurate the conclusions and recommendations become.
Analyzing big data stored in internal data repositories and warehouses is becoming standard in the industry, but AI-enhanced analytics solutions – like OpenText™ Magellan – can also evaluate the unstructured content in EIM systems to unlock the value in Big Content, such as repositories of contracts, business plans, proposals, and incident reports. These documents often contain more context and more valuable insights than structured, transactional data because it is written in natural language and reflects human opinion, intention, emotion, and conclusions.
By leveraging AI to analyse unstructured content, organizations can gain far more from the information, such as intent and sentiment, which adds valuable context to any analysis effort. Organizations can leverage AI-enhanced analytics to discover insights in orders, invoices, cases, settlements, statements and remittances as well as the portfolio of customer and trading partner documentation. This is something that the Financial Services industry has wanted to do for some time.
How can AI-enhanced analytics add value to payments?
AI has been seen as a means for automating and driving inefficiency from payments processing – especially payments exception processing which is still heavily manual. Yet, AI-enhanced analytics has the potential to deliver the benefits that payments professionals within banks are looking for from their AI investment.
The benefits were brought out in a survey from Finextra where 70% of respondents agreed that there was a need for more awareness of how to apply AI technologies in transaction banking. However, when asked, it wasn’t process efficiencies but business imperatives that topped the list for expected benefits. Respondents felt that time to market was a bigger challenge than regulatory compliance. In addition, product innovation, time to market and customer retention were the key areas for investment.
These are all areas where AI-enhanced analytics can provide a level of insight that equates to real competitive differentiation. Within product innovation, an organization can see from the behaviors and sentiments of current customers which product variations are likely to be the most successful prior to creation – reducing the risk and cost of new launches. AI-enhanced analytics facilitates other key processes – such as onboarding – to significantly shorten time to revenue. Finally, deep understanding of customer behaviors can be delivered instantly to key people, like Relationship Managers, to identify where accounts may be at risk and suggest actions to redeem the situation and improve client retention.
There is a good deal more to cover about the value AI-enhanced analytics can bring to your payments business. I just wanted to whet your appetite. If I have then you should join us at our Open Theatre Session at SIBOS on Tuesday October 17. I hope to see you there.