Leveraging STP in KYC
In KYC and AML, data forms the bedrock that important decisions are based on. Without uniformity in that data, it is very hard to move beyond the manual data processing that is undertaken in even the simplest of tasks, and that is after you’ve been to all the discrete data source providers in order to collect that information via various and different means.
Normalizing the data from each of the discrete sources, presenting them as a source and providing a single means to access the data, means you have removed the lion’s share of the leg work involved in the foundational KYC/AML activity. Furthermore, when normalized data is used, reinforcement learning can be applied to the data sources during pre-processing, further reducing the need for user workload.
KYC departments within financial institutions are now looking to straight through processing (STP) – the method of speeding up processing without the need for manual intervention, a process that has long been established with the payments industry. STP is made possible by the uniformity of the data, and reduces the manual overhead to between 0-20%, where the 20% represents data that is not as readily available.
Capabilities in STP are improving dramatically. Whereby investigators face a vast data landscape, the need to identify patterns of behaviour to stop risk can be enhanced through advanced machine learning techniques. Beyond an initial fraud detection check, machine learning models can best stop high risk alerts and flag these for further AML investigation; STP can be leveraged in ‘real time’ allowing normalized data to be directed towards the correct entity. Low risk alerts are automatically straight through processed, or raised to an investigator if they are considered high risk entities, so that the correct due diligence can be carried out by experienced human investigators on elevated cases.
With the correct data attributes and STP automatically supporting analysts, a more consistent perpetual client monitoring process can be achieved.
Arachnys has made great progress in the quest for perpetual KYC and the straight through processing of corporate data.
A user can search for a company in a particular jurisdiction, and apply an overlay – a set of policy instructions to retrieve KYC data. In most cases however, banks will query this information programmatically through our API.
Where multiple versions of the entity are found, an analyst can select the right entity through a call to action. With an appropriate corporate registration number upon query, all of the information will be processed automatically.
All of the official corporate information that is required for the policy is populated in the background, such as date of incorporation, industry, address and adverse media signals. Additional to these pieces of data, Arachnys pulls through shareholder information and related parties, in order to assist organizations to automatically calculate UBO information. Users can also drill down on a particular shareholder to see the other relationships they have, or for example to run automatic PEP checks.
All of these data attributes that are governed by the KYC policy can then be monitored, updated and straight through processed into the customer’s architecture to avoid the need for periodic review.