5 Steps to Choose a Machine Learning Platform for Risk

Whether you’re shifting away from a legacy system or starting new, focusing on these five areas will set you up for success.
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Ask vendors the right questions

Is the platform future-proof?

How much control do you have?

Do you understand how the system arrived at its risk score?

Does the system enable omnichannel data?

Is it scalable?

Is expertise included in the price?

How specific does it get?

Does it offer flexible case management?

Is it self-configurable?

Determine the best deployment strategy

You can host your solution one of three ways: on your premises, in your vendor’s cloud, on your own cloud. Determine the deployment method that works best for you; there isn’t a one-size-fits-all approach.

Ask vendors the right questions

FI assumes operational and maintenance responsibilities, including procuring hardware, licenses, and QA governance inclusion of the system into existing frameworks.

Platform sits on the FI’s existing infrastructure.

Hidden/unanticipated operational costs can contribute to long-term spending (maintenance scalability).

Ongoing troubleshooting issues and remediation are less effective due to environment access and privilege restrictions applied.

Licensing agreements may limit FI’s scalability agenda.

Vendor Cloud

Vendor assumes responsibility for operation and maintenance
of the platform.

No hidden costs in lecensing agreement.

FIs have transparency into the platforms operational costs
and how it can scale.

No platform operation and maintenance.

Vendor manages troubleshooting and remediation.

Ensure the AI Platform is Unbiased and Makes Fair Decisions

Consider ethical AI questions at the beginning of your machine learning journey for fair risk decisions. Making AI ethics a priority can save your organization valuable time and money.

Focus on ‘Fairness-Awareness’ from the start of your AI journey so you develop models that are both fair and accurate.

Ensure your vendor provides easy-to-understand and clear explanations for how the model reached a decision.

Regularly audit models for bias and provide the reports to regulators when submitting decision-making reports.

Find a Vendor That Can Remove Silos Between Fraud and AML

Aligning fraud prevention and anti-money laundering solutions creates a more nimble approach to fraud prevention, realizes lower total cost of ownership, and enhances AML compliance. Benefits include:

Elimination of data silos

Upgraded operations

Boosted AI performance

Improved customer relationships

Ensure a Seamless Machine Learning Implementation Experience

Implementing a machine learning platform requires careful planning and preparation.
Here’s how to ensure a painless shift to a machine learning platform.

Prepare for big changes

Get a handle on your data

Look for a partnership, not a sale

Designate ownership of the platform

Think flexibly

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