Intelligent automation fueling digital transformation in banking: Yapi Kredi
Is intelligent automation fueling digital transformation in banking? Qorus and UIPath interviewed Cemal Enis Ös, Artificial Intelligence and Robotic Processes Manager at Yapi Kredi, to find out more about the subject and what the Turkish bank is doing in terms of intelligent automation.
Is intelligent automation fueling digital transformation in banking? Qorus and UIPath interviewed Cemal Enis Ös, Artificial Intelligence and Robotic Processes Manager at Yapi Kredi, to find out more about the subject and what the Turkish bank is doing in terms of intelligent automation.
How big a part does intelligent automation play in your organization’s digital transformation strategy today, and how much will that change in the next two years?
There are 170 processes in which intelligent automation applications are used in Yapi Kredi. Through these automated processes, an average of 23 million transactions and controls take place per month. The development and management of intelligent automation applications is carried out by three teams consisting of 35+ people.
Intelligent automation technologies are utilized in the bank's sales, operations, quality, compliance and risk, internal control and internal audit functions. They are placed at critical points of the banking processes, ranging from new product release to KYC controls, non-performing product intelligence, fee collections, fraud detection, and financial analysis.
In the next two years, we are investing in democratizing the production of intelligent automations, training and maintenance of algorithms, in order to produce IA scenarios more widely and faster. We are planning organizational structures in which employees are included in the delivering of IA processes. In this way, the number of business processes in which robots, artificial intelligence algorithms and humans work together and the amount and quality of unit output are increased, and the share of IA projects in the total digitalization program will likewise grow.
To what extent do you think intelligent automation is transforming banking today and will over the next two years?
Because intelligent automations have no time limitations and make transactions faster, the way business is carried out and the banking processes are changing. As the prevalence of robots and algorithms increases, we see that banking roles shift more to sales, product design, and customer analytics functions.
When the information processing and operational steps required to do the job are done by robots and algorithms, the amount of work produced by humans increases. For example, in a complaint management process with manual steps, an agent can deal with six tickets per hour, while 30+ can be completed with the advantages provided by robots and algorithms.
On the other hand, new tasks such as robot/algorithm training, data labeling, and verification of robot/algorithm outputs are undertaken by humans in order to maintain robots and algorithms. We think that in the next two years we will see a work environment where employees quickly train their own robots/algorithms and develop virtual assistants for themselves.
What areas of your business have benefitted most from first generation intelligent automation technologies to date? Could you share an example of the most successful implemented project?
As mentioned before, we use intelligent automation applications in core banking processes such as sales, operations, credit and legal processes such as compliance, risk, internal control and internal audit, and support processes such as human resources, outsource service management and purchasing. Previously, our projects in the areas of auditing and financial analysis were awarded the Banking Innovation of the Month by Qorus. Our audit project is important in terms of changing the way credit audit is done and contributing to risk reduction. Our project in the field of financial analysis, on the other hand, is one of the first applications in the sector in terms of reducing the duration of the work done dramatically and freeing up time for people to evaluate quality.
In your opinion, what are the main obstacles in gaining scaled benefit from intelligent automation? (Too many competing priorities, IT resistance, insufficient return on investment, etc.?)
Performing the tasks such as accessing data, labeling the data and verifying the outputs of algorithms/robots centrally and by people who are more competent than needed, is a challenge to scaling and growth. These tasks, which we can call support tasks in the intelligent automation lifecycle, can be performed by employees in business processes or business experts without information technology experience. Another challenge that complicates scaling and growth is that IA infrastructure technologies are not aligned with technology production and business design. Managing infrastructure requirements such as data storage, maintenance, transfer to data warehouse environments, authorization and access within standard processes causes this part to remain cumbersome in the intelligent automation lifecycle.
We see that Automation 2.0 (newer IA technologies) is now getting a wider adoption in banking - what impact are these technologies having now / will they have in the next two years? Which areas of your business do you think will benefit most from them?
With the wide use of intelligent automation technologies, a culture of working with robots and algorithms began to emerge. People performing standard tasks in business processes learned what to expect from robots/algorithms or what these new colleagues can do. Now we see people making suggestions on how business processes can be automated and how robots/algorithms can benefit their work. Producing automation ideas has become part of their everyday work.
In the near future, we will see an environment where people take an active role not only in developing the proposal but also in the implementation of the proposal. Then, training his/her robot/algorithm and increasing its performance will become a part of an employee's daily life. In this new environment, we expect to see prominent examples in areas such as product application evaluation, AML controls, banking operations, customer service, and non-performing product tracking/collection, where similar tasks are frequently performed.
Can you specify some of the key issues that you are facing with IT application testing (cost, manual nature of the work, release time, employee experience, quality issues/defects)?
The use of robotic automation technologies in the running of the regression tests carried out to see the effects of information systems changes will improve the test quality. On the other hand, due to the manual nature of the work, there is a need to run scenarios closest to the user in order to test the applications used to ensure information security. By operating a wide variety of risk scenarios, it can be validated whether the designed alerts can be created or not.
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