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Customers in the financial services sector are more demanding today than they were a few years ago. They want to access or retrieve relevant information faster and in a targeted manner; they also expect easy and hassle-free workflows for applying for contract changes. This puts enormous pressure on the financial services industry. The expectations of customers must be met or, better yet, exceeded, and this must be done while reducing administrative costs and taking into account changing market conditions and general frameworks.

Admittedly, the topic of processes and process modelling is not new – especially in the financial services sector. Nevertheless, the topic is still of importance. All the more so since this can be an important building block for companies to achieve their business goals and pushing forward their digital transition.

Any company handles a number of (business) processes, particularly so the financial services sector. One such example is the processing of business transactions. In practice, the processes and procedures are known to those involved, but have not been documented in the past, or only in broad strokes. This results in a lack of transparency and makes it much harder to grasp the opportunity to tap into potentials for improvement to optimise processes. Process modelling can be of help in this context. The process and its sequence are surveyed. A chart is created to show which activities need to be done in what order and which actions are required by whom and how. Various methods can be used to implement process modelling. Companies often opt for a standard notation – such as BPMN 2.0 – and use appropriate software for the graphical representation of the processes.

Process modelling goals

Companies may pursue all sorts of objectives with process modelling. These include creating transparency, reducing costs, increasing efficiency, improving or optimising processes, eliminating unnecessary activities, reducing complexity and reducing cycle times.

A decisive advantage of process modelling (and a common goal) is to assess whether and, if so, in which process and for which activities supporting technology is viable.

The concrete goals a company wants to achieve by means of process modelling strongly depend on the individual requirements and should therefore be defined before launching this project.

Process analysis

After the process has been modelled, it is analysed based on the modelling objectives, weaknesses are uncovered and appropriate measures are derived and then implemented.

For example,

  • a financial services company wants to reduce the time it takes them to process a specific business transaction by 50 per cent. Typical questions that guide the analysis stage include:
    • Are all activities necessary / which ones can be dispensed with?
    • Do activities and tasks occur redundantly, and can they be dispensed with fully or in part?
    • Can service level agreements (SLA) help to reduce the process time?
    • Can the process or individual activities profit from digitisation measures?
    • What technologies can be used to improve or optimise the process?
    • What requirements must be met in order to be able to use the relevant technology?

Processes and digitisation

Nowadays, there are a number of ways to digitally support or even implement processes and/or activities. Which ones come into question depends on a number of factors that need to be taken into account. These can include the existing or future target system landscape and the required hardware, the results of cost-benefit analyses, the amount of data as well as the required data preparation and the availability of staff capacities for the integration or development of a software tool. I will describe some of the technical options based on three concrete examples.

1. Self-service

The company sets up a customer ‘self-service’ area on its homepage. Customers can log in using their contract data and password and call up or request information about their existing contracts, download documents and also request contract changes.Examples:

  • Customers notify the financial services company of their new postal address via self-service using an online form.
  • Customers download a document they can use to notify the financial services company of rights to insurance benefits in the event of their death.
  • Employers notify the insurance company of an employee’s termination of service via an online form.
2. Artificial intelligence

Artificial intelligence (AI) and solutions – such as chatbots and automation (robotics) – can reduce the workload of employees. Insurance companies can implement specific solutions such as chatbots that reply to standard queries. The benefit for customers is that they can immediately get in touch with the company at any time of the day or night, regardless of whether they prefer to contact the company over the phone, using their smart speaker, via e-mail or using social media channels. This means that customer service staff can focus on the more complex cases rather than deal with the daily flood of easy-to-solve standard queries. This allows insurance companies to offer a higher quality of customer service without increasing their effort. By means of automation (robotics), incoming mails and the like can be distributed directly to the virtual inboxes of the responsible employees using automated text recognition. A processing priority can be automatically assigned to each incoming mail, depending, for example, on the type of business transaction or the sales partner.Examples:

  • The customer would like to receive an overview of the expenses made in the last month from his bank. This is created automatically without the need for manual intervention by a bank employee.
  • The customer sends an insurance application to the insurance company by e-mail. Based on automated text recognition, the e-mail is directly forwarded to the virtual inbox of the responsible department with the highest processing priority.
3. Dark processing

The automated processing of business transactions is called dark processing (sometimes also referred to as black-box processing). The entire process is fully automated, i.e., without manual intervention by an employee.Examples:

  • A sales partner submits an application for pension insurance of his customer to the insurance company. The application is entered into the system, processed and an insurance certificate is created, which is sent directly to the customer. There is no need for manual processing or intervention by a staff member.
  • A customer sets up a mortgage account with a bank via its website. The data and information entered are automatically routed to the bank’s core system. The account is opened automatically, and the customer receives a corresponding message.

Challenges

A challenge in process modelling and analysis is the required assessment of individual activities and work steps as well as the derivation and implementation of measures. It is not uncommon for employees who are involved in process modelling to say, ‘We’ve always done it that way’ or ‘It has grown historically’. It takes a little practice and social skills to break these thought patterns. It is therefore important to have process modelling carried out by trained employees with practical experience. They must not only be familiar with the topic of modelling, but also be trained to steer clear of the ‘old beaten track’ and to motivate other involved parties to leave their comfort zone.

In addition, they must have technological competence, including distinctive (industry-specific) specialist and business know-how, in order to be able to reach the defined process modelling goals as well as to back them up them with customised IT strategies where necessary.

Would you like to learn more about exciting topics from the world of adesso? Then check out our latest blog posts.

Picture Sandra Weis

Author Sandra Weis

Sandra Weis is a Senior Business Consultant at adesso. She has decades of experience in project management and as a business analyst in the life insurance sector, including occupational pensions and biometrics.

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