The Potential for RegTech to Address De-risking. Jim Woodsome & Vijaya Ramachandran Center for Global Development

Transcription

1 The Potential for RegTech to Address De-risking Jim Woodsome & Vijaya Ramachandran Center for Global Development

2 The Problem Worldwide decline in correspondent banking relationships Small and developing countries especially hard-hit Several contributing factors One major factor: rising cost and complexity of AML/CFT compliance Correspondent banking especially vulnerable to cost squeeze: A high-volume, low-margin business ML/TF risks challenging to manage, due to information asymmetries in intermediation chain Public and private stakeholders looking for solutions without watering down regulatory expectations

3 Technological Solutions 5 innovations show particular promise: 1. KYC utilities 2. Big data 3. Machine learning 4. Blockchain 5. Legal entity identifiers

4 KYC Utilities (1/2) Central repositories for customer due diligence information Centralization of information collection and verification reduction in bilateral information exchanges (+1.3m bilateral CB relationships) Several KYC utilities launched in 2014 and 2015 Information provider uploads information for free; information consumer pays to access information

5 KYC Utilities (2/2) Example: SWIFT KYC Registry Services correspondent banking sector (among others) Baseline dataset determined by major correspondent banks 4,000 participating financial institutions Members spend 45% less time on due diligence than before Add-on services include the SWIFT Traffic Profile, which allows correspondent banks to see payments traffic to and from high-risk jurisdictions

6 Big Data (1/2) Data that is high volume, high velocity, and high variety Requires different hardware, software, and analytical solutions AML/CFT compliance requires data from many sources (internal and external) Data is typically spread out across organizational silos Big data applications can consolidate information in one place, reducing time spent searching for and aggregating information Advanced analytics engines can identify previously undetected patterns and relationships in the data Visualization can make complex datasets easier to understand

7 Big Data (2/2) Example: How big data can make AML/CFT compliance more nimble Relational databases require data to be structured prior to loading This approach assumes both a.) what queries a user will run; and b.) what data will be needed to answer these queries When a new threat emerges (e.g., when the Panama Papers leaked), it can take banks days or weeks to assess their exposure Big data applications structure their data on query, not on loading This allows for much faster turnaround times on new lines of inquiry from hours or days to minutes

8 Machine Learning (1/2) A type of artificial intelligence Enables computers to learn without being explicitly programmed Supervised machine learning: analyzes a dataset to predict a predefined output Unsupervised machine learning: explores a dataset looking for patterns and relationships Can be used for a wide range of compliance functions, including Customer segmentation; and Transaction monitoring

9 Machine Learning (2/2) Example: Reducing false positives in transaction monitoring Majority of alerts generated by traditional transaction monitoring systems are false positives Root problem is hand-coded rules Machine learning algorithms can be used to develop more accurate predictive models Ayasdi, an AI firm, helped a major correspondent bank reduce KYCC investigations by 25 percent while also discovering previously undetected risks

10 Blockchain (1/2) Transactions are recorded in time-stamped blocks Each block is connected to previous blocks, forming a chain Transactions are confirmed and stored by all users on the network, making the ledger difficult to tamper with Can potentially be used for a range of compliance functions, including KYC and cheaper and more secure international payments (including remittances)

11 Blockchain (2/2) Example: Blockchain for storing and sharing KYC information KYC Chain and Tradle, two start-ups, allow customers to record KYC verifications in a digital wallet stored on a distributed ledger Information can be shared with other financial institutions as requested Could reduce duplication of effort Puts customers in charge of who sees their information

12 Legal Entity Identifiers (1/2) Unique alphanumeric identifiers like barcodes for legal entities that engage in financial transactions Connects to a reference dataset in a public database Operates on a federated system May be used as a starting point for customer identification and due diligence May support interoperability with other AML applications for KYC, facilitating automation and information-sharing Nearly 600,000 LEIs issued worldwide

13 Legal Entity Identifiers (2/2) Example: LEIs for KYCC In future, LEIs may be included in payment messages for easier and more robust identification of originators and beneficiaries Would enhance transparency of correspondent banking transactions But would require changes to payment messaging formats Would also require banks to update their IT systems, as well as more widespread adoption of the LEI outside the financial sector

14 Conclusions RegTech has the potential to contribute to substantial efficiency gains and improved capabilities in AML/CFT compliance May alleviate certain pressures on correspondent banks to de-risk (over the medium term) Regulators need to become more familiar with these technologies Banks need more room to innovate Certain regulations may need to be revised

15 Thank You.