Commercial Banking: Credit Risk & Anti-Money Laundering (AML)

AMPLYFI empowers you to streamline the assessment and management of potential losses from lending activities. It supports the development of risk models and portfolio management strategies, extending to monitoring individual organisations at origination and during their lifecycle.

Key benefits

Increased Technical Profits: Achieve better origination and risk pricing through more accurate assessments, taking a proactive approach to managing risk throughout the product lifecycle.
Lower Administrative Costs: Automate origination and in-life credit risk monitoring to reduce manual interventions, enabling earlier detection of risks through granular scoring and evidence-based assessments.

Example Use Case

"We need to build a new credit risk model that incorporates alternative data sources to better assess creditworthiness in emerging markets, whilst protecting ourselves against fraud and AML exposures."

Credit Risk & Anti-Money Laundering: Overview

The First Use Case is Key	Smaller Organisations are often harder and less valuable to assess, but are more aligned with scaling Larger Organisations are often easier and more valuable, but well covered by existing manual solutions
Human + Machine	Combinations of Human + Machine work best, especially combined with clear transition and escalation programmes Think about when to allow more straight through processes and when to spend human intervention time to improve processes
Scale in Steps	Do not expect to process more transactions than with a well-trained human team at first - time is needed to establish the new processes Scaling steps should align to the points above - use case by use case, with humans stepping out of specific areas
Augment Over Time	Design your Credit Risk processes to be self-improving and use previous data and new alternative data sources to expand your ability to price risk and react to changes in the market

Case Study: NatWest Credit Risk Scores

Award winning tools to enable better Commercial Due Diligence

Challenge

NatWest faced difficulties in keeping up with risk-related events tied to their clients, despite hiring additional analysts. They required a solution to monitor risks more effectively and proactively.

Approach

AMPLYFI developed a machine learning model to detect risk-related activity, including mentions of fraud, money laundering, and subtle risk indicators like organisational changes and leadership transitions. This model used global news feeds, internet scans, and finely tuned linguistic pattern recognition to deliver accurate results.

Solution

The risk model was integrated into AMPLYFI’s platform and rolled out to over 50 analysts. Shortly after adoption, NatWest’s Risk Management department won the Risk.Org Award for Risk Management & Mitigation due to increased efficiency and accuracy.

“The most refreshing thing is the fact we now have a pre-emptive rather than a reactive approach to portfolio management,”

-Benedetto Fiorillo, Head of Portfolio Risk Mitigation at NatWest.