Skip to main content

Digitizing KYC Processes

digitizing kyc processes

Digitizing KYC processes is necessary to prevent human error and better identify fraud.

Context & Challenges

KYC processes rely heavily on human labor, incurring significant labor costs and potential for error, with little standardization between banks or within banks, and with records often being individually maintained. This requires a significant workforce to execute, which accounts for some but not all of the costs at hand, and, in 2016, banks took an average 24 days for customer on-boarding.

On-boarding an individual customer could cost anywhere from $20,000 to $30,000. Major financial institutions spent up to $500 million annually on KYC, with an average cost of $60 million for each institution, with it also increasing costs of customer on-boarding, with a 19% increase from 2016 to 2017 and 16% from 2017 to 2018.

Currently, ongoing monitoring is an essential element of effective KYC procedures. However, to effectively monitor bank accounts involves setting up daily and monthly limits for transaction amounts and constantly checking for unusual bank account activity. Both of these tasks rely heavily on human effort, and as such, come at a high labor-cost and with great potential for human error. The result is that the current process is inefficient and in need of increased automation:

—  On-boarding Request – This part of the process is based on pinpointing, pitching, and closing new customers and can take 3-6 weeks while costing $2000-5000.

—  Document Gathering – Collating the relevant documents is a largely manual task involving both identification and validation, taking 1-4 weeks while costing $1000-5000.

—  Background Verification – To validate client information involves cross-referencing data across a variety of databases, 3rd-Party providers, and government agencies which takes up to 2-4weeks and costs $1000-5000.

—  Credit Terms Setup – Performing due diligence on clients’ credit and deciding credit limits relies on manual processes, and the validation of data across multiple parties which can take as long as 1-3 weeks and cost $500-2000.

—  Agreement Management – Completing legal due diligence and negotiating terms requires manual intervention and coordinating consent from various parties, a process lasting 1-3 weeks and costing $1000-3000.

—  Account Setup – Establishing client accounts on banking systems involves manual effort, takes 1-2 weeks and costing $500-3000.

—  Tracking and Data Archiving – Monitoring client transactions in real-time and developing a data trail is a time-consuming and complicated process, it’s ongoing and costs an initial $1000-3500 in addition to recurring costs.

—  Analytics & Cross-selling – Downstream processes like analytics and cross-selling depend on manual processes and are ongoing commitments costing $1000-$3500 initially in addition to recurring costs.

Parties involved:

—  FI Employees
Employees at financial institutions must cooperate with demanding compliance processes and perform often irritating and time-consuming tasks in order to fulfill KYC processes, meaning that their time is not always efficiently distributed.

—  Customers
Customers must undergo lengthy, and often repetitive KYC processes that require them to fill out burdensome amounts of paperwork. Often, the requirements of this process delay customers from opening of bank accounts, which can jeopardize heir finances.

—  Financial Institutions (FIs)
Financial institutions invest heavily into KYC processes and often do not see returns reflective of this. KYC compliance is expensive, labor-intensive, and often ineffective, with frequent security breaches and errors.

—  Regulators
The regulation of KYC processes is a complicated and labor-intensive process, with the lack of standardization across FIs making it difficult and expensive for any regulator to achieve a holistic overview of any situation and, as such, regulate it.

Digitizing KYC processes on STRATO Mercata.

Solution Benefits

Smart contracts allow many traditionally human-dependent processes to become automated, reducing time-costs, labor-costs, and potential for error. In the case of KYC document storage, banks can work with a STRATO Mercata, which would store and share KYC-related data in a consolidated and secure manner. A financial institution can save five times their investment by digitizing KYC processes with automation technology like blockchains.

Moreover, one bank or financial organization’s verification of clients would be visible to others as blockchain would drive more robust digital identities, reducing the amount of time and money banks currently spend on the verification process. In addition, client data could be standardized between FSIs by implementing the same blockchain, ensuring data consistency and ease of use. This increases transparency and fraud prevention, as the potential integration of automated alerts and responses in the event of unusual activity into smart contracts. An Accenture study reported that blockchain could generate cost savings of 50% on central operations, including KYC, as well as up to $100 million in savings every three years from automation.

To recap, digitizing KYC processes on STRATO Mercata provides the following benefits —

—  Reduce the potential of error through automation, and ensure all data is updated in real-time.

—  Through automated immutability, time-stamping, and traceability, reduce regulatory burden.

—  By creating a single, distributed ledger drive industry standardization and allow for seamless communication between parties

—  Automate certain actions through smart contract logic, reducing the need for labor

—  Prevent fraud by inherently verifying all data

Additional features include:

—  RESTful APIs for direct connection of IoT devices such as bankers’ devices to the blockchain network

—  Identity Management, OAuth, and SSO capabilities for simplified IoT authorization and user login

—  Privacy via private chains to keep sensitive data private and control who sees what data

—  Enterprise Data Modeling for integration into existing data systems and to ensure interoperability