This article was co-authored by Mile Dragosavac.
Financial institutions are obliged to perform know your customer (KYC) due diligence when on-boarding a new customer, and on a periodic basis with existing ones. The goal of periodic reviews is to keep information updated in order to ensure that the risk score reflects the current situation of the customer.
In the case of business accounts, the KYC review includes the retrieval of the company vitals (commercial register) to verify changes in the data. These may include the company name, address and key management personnel. Most of these checks are manually performed by a KYC specialist. The process is long and repetitive, involving log-in to the commercial register site, downloading the data, updating it with new information and archiving the file.
Financial institutions usually have 60 to 80 full time employees to perform KYC review. This is valuable time that could be spent on more meaningful activities.
The use of robotic process automation (RPA) could assist these specialists by undertaking repetitive tasks faster and with minimal errors, leaving them with more time. At Infosys Consulting, we advise a number of financial institutes that are new to RPA.
In this blog we will elaborate upon our methods of implementing this technology for financial institutions, its prerequisites and finally its benefits for the organizations.
The implementation of RPA begins by identifying core processes and the challenges therein one by one, in a modular fashion, rather than automating all the processes at the same time.
The main advantage of a modular approach is tangible results and the experience that an organization derives by using RPA in batches.
A critical step in starting the use of RPA is picking between well-established software solutions such as (UiPath or blueprism) or the use of open source code like Python. Both the tools provide a huge library with different code options to automate the processes, but the advantage of the former is that everything is already coded and can be used as plug and play. The use of these tools usually has a license fee. The open source code, on the other hand offers more flexibility while automating processes but it requires expert coding skills.
During the KYC reviews for companies, the commercial register extracts are usually downloaded as PDF files. This is not an ideal format for RPA. Although PDFs are easier to read they are not suitable to summarize information or tabulate data. In addition, the information is read one at a time for each check and thus no database of all previously downloaded excerpts is created.
With the help of a tabular database, analysis can be automated across companies and aberrations can be detected real-time.
The history of the shareholders can also be recorded and presented comprehensively.
The prototype shown here reads a list of uniquely identified companies provided by internal systems. The user can check the list and then start the process. Afterwards, the trading entries of the listed companies are downloaded one after the other in a predefined format. In an XML format, the application can parse the document to get a tabular overview of the information. The next step is to check whether a shareholder is included in an Office of Foreign Asset Control list or any other list. Finally, all of the data is stored in a standardized format in internal systems for further and subsequent centralized analysis.
This process can be programmed to recur on a predefined schedule and/ or triggered by specific events like an incoming e-mail or initiated by a person. As mentioned earlier, the KYC specialist can then focus on the overview of customer data in order to gain a deeper understanding of complex ‘beneficial owner structures’.
One of the advantages of a modular approach is the flexibility to respond to any type of challenge over time.
This also opens the possibility to add other functions, such as network analysis that helps us to identify the links between individuals in our database, or if a legal representative is working in other companies. Through this approach the primary database can be enriched with other data sources, such as transaction data, to create a more comprehensive network analysis. Once the machinery is up and running, the decision makers are accustomed to its functions and results start to show, new ideas can emerge that are relatively easy to implement.
Principal, Infosys Consulting
Gerardo Salonia is a principal within our financial services practice in Germany with a focus on compliance, AML and KYC areas. He has extensive consulting experience within the e-commerce and financial services domain. Gerardo has enabled several European companies and financial institutions to overcome the challenges posed by disruptive technologies and transform into digital-oriented organizations. Gerardo holds an MBA in business administration from the University of Mannheim. He is a certified AML officer and has a risk management certification from the Goethe Business School – Frankfurt University.
Senior Consultant, Infosys Consulting
Mile Dragosavac is senior consultant within our Artificial Intelligence & Automation practice at Infosys Consulting, Germany. His focus lies in business intelligence topics leveraging data driven decisions using machine learning. He also has extensive banking experience, mainly in the credit risk controlling area and multi asset management. Mile holds an MBA in economics from the University of Mannheim and is certified applied Data Scientist from University of Michigan.