Over the years, banks have faced/are facing some major roadblocks like security threats, lack of customer intelligence, lack of technical expertise, etc. On top of it, the increased customer demand for a better  customer experience and the competition is adding pressure of major player.

Although, banks are trying to be digitally resilient, there is still one area which hinders the growth of digital banking massively and that is the manual IT operations. The increase in digital offerings also means probable increase in the volume of tickets raised, as there would be issues like system crashes, missing payment confirmations, inconsistencies during generations of e-statements, etc. Tackling such issues manually is quixotic as it takes a lot of human efforts and operational costs.

Need for AI-based Service ticket Analytics in Banking

One might ask a question-

Is handling Banking IT operations manually a daunting feat?

Here’s an example to answer the question:

Imagine a scenario where you receive a ticket or an incident of service failure during a payment transaction and you have to predict the priority, estimate ticket resolution time, and assign it to the right team. Add a thousand to that number of tickets for a day. Practically, is it possible to perform this task with perfect accuracy?

No, because humans can predict all these details based on their experience and this repetitive task would only make the process monotonous.

Due to above setbacks, financial enterprises have realized that modern problems require modern solutions. Hence, the concept of AI-based ticket Analytics has become a favourite among bankers and fintechs. AI offers banks the capability to analyze patterns in business data in the form of customer analytics, risk analytics, ticket analytics, etc., which leads to better customer intelligence and operational efficiency in complex business processes.

Common challenges faced by banks in manual ticket management are:

  • Huge inflow of tickets and the inability to explore the data in these tickets leading to lower productivity.
  • Rules-driven ticket SLAs which adds to the already existing pressure to resolve the issue.
  • Lack of accurate data to measure the cost per ticket and other metrics.
  • Repetitive ticket types which causes additional delay due to lack of smart solution.

Due to the above limitations, AI-based ticket management is slowly becoming a demand as it processes the tickets using past data, hence, provides accurate metrics, reduces human efforts, and improves overall productivity. Apart from these, the ever increasing customer demand calls out for a speedy and accurate business process. By analysing banking data patterns like customers, transactions, products, feedback, etc., AI models provide faster and error-free end product.

Growing number of banking applications and offerings directly impact the number of tickets raised. In order to run the banking system with a “smooth and well-oiled engine”, banks are being more attentive on effectively managing the service tickets.

How AI-based ticket management works?

AI-based ticket analytics provides a holistic view like number of tickets raised yearly, weekly, monthly, comparison of the tickets year-wise, month-wise, quarter-wise based on priority, category, location, etc. This is called as ‘Ticket Profiling.’ AI helps to understand and cluster service ticket data to provide actionable intelligence on application issues that need to be classified, prioritized, and permanently solved. Given a single ticket, AI can determine following analytics:

1. Ticket Priority
Based on the past data of similar tickets and using pre-determined variables, AI calculates the priority of the ticket.

2. Ticket Resolution time
AI calculates ticket resolution time using Machine Learning based on factors like ticket segmentation, assigned team, past turn around time, reassignment count, etc.

3. Ticket assignment group prediction
AI learns the patterns of the ticket assignment in the past and using this data, it predicts the group assignment for the new ticket.

4. Ticket business impact prediction
Using Natural Language Processing, AI interprets the description of the ticket and analysing the context, it predicts the business impact.

5. Ticket segmentation
One of the interesting features of AI is the ability to segment the tickets into similar groups which drastically reduces SLA time. Once a new ticket is raised, AI segments the tickets into different groups which help faster debugging.

6. Ticket volume forecasting
Another feature which helps banks draft an efficient plan for the future is the accurate ticket volume prediction by AI. Using independent variables and past data references, AI predicts the forecast of the volume of tickets for the succeeding month, quarter, or year.

7. Ticket insights service
The ability to improve the business of any enterprise using AI is due to the useful insights that AI can provide. Even in ticket analytics, AI provides deep insights of the ticket patterns which help enterprises to plan better actions on ticket management.

Benefits of AI-based ticket analytics

A report from Everest states that, AI in ticket management has led to the following results:

  • In mortgage lending, the ticket resolution time with respect to customer onboarding has reduced by 20-40% and in agent onboarding by 15-30%
  • In Credit reporting under mortgage lending, MTTR has reduced by 20-40% and 5-15% improvement in service desk tier 1 resolution rate
  • Under trade execution, there has been 30-50% reduction in MTTD and MTTR in middle and back office input feed delay

The major reason for the above improvements is due to automated ticket resolution using AI. This clearly implies that AI-based service ticket analytics is imperative especially for banks if they want to establish long-term customer relationship. Here are some of the benefits of AI-based service ticket analytics:

1. Improves overall customer support
As AI provides a comprehensive view of the tickets generated, it becomes easier to debug the issues and hence, is possible to provide faster response to the customers. Also, AI predicts a definitive SLA timeframe which engenders customer trust.

2. Increases scalability
Repetitive ticket types are the major time killers and eventually it reduces the productivity. Thanks to AI which segments the tickets in seconds, reducing time and effort and thereby increasing efficiency.

3. Better customer engagement
With deep insights with AI, banks can understand the critical aspects of their core banking system and enhance it to cater to the customer needs. This eventually promises long-term customer engagement.

4. Budget-friendly
AI performs a lot of manual and strenuous tasks within seconds which otherwise would take hours and days manually. With so many offerings, AI-based service ticket analytics are affordable to any financial institutions.

Conclusion

With the competition continually rising amongst bankers, fintechs, and other financial organisations, it is imperative to be adept with customer service. Thus, ticket management is an area that deals directly with the customers and hence, needs to be as advanced and smart as possible. AI-based ticket service analytics leverages strong technologies like Machine Learning, Natural Language Processing, etc., and with a combination of multiple algorithms like Random Forest algorithm, Prediction algorithm, etc., it offers plenty of options like chatbots, virtual service desk agents, etc., to help banks provide better customer service and seamless ticket management experience.

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