Why utilizing your own data is a competitive advantage
When it comes to customer onboarding and fraud transaction monitoring, there are multiple benefits to utilizing internal data to boost your real-time decision-making.
First, you know your customers best. Every day, your company collect unique, valuable data points from your customer base. These data points paint a very detailed picture and describe an action of a user more accurately than any external, generalizing data service could. Using these data points in custom models brings your user interaction into the proper context. It also makes your automated decision-making more accurate and efficient.
Secondly, utilizing custom models results in much faster decision-making and is often the only way to use machine learning in sub-second real-time decisions. Due to network latency and technical implementation, external data vendors usually need seconds to respond to a query. Self-developed, co-hosted models have the advantage of instant responses and better control of the overall outcome. In addition, they can provide higher accuracy due to customization to your company’s individual needs.
Finally, using your propriatary data can be a real differentiator. They give you a competitive advantage in a world where digital offerings become increasingly similar and price differences minimal. Customized machine learning and analytics helps create unique onboarding experiences by reducing fraud numbers at the same time. Eventually, this can boost your customer onboarding program and positively impact your growth and revenue.
Why you need a fraud and risk platform built for custom ML
Okay, you have the correct data and organizational buy-in to build your own machine-learning models. You identified customer onboarding and risk mitigation as the prime use case, and your skilled data team developed well-working models in theory. Now, even if you have built custom model artifacts, utilizing them in production is still the biggest challenge. Factually, most machine learning projects eventually fail, not of talent or resources, but due to the lack of efficient utilization in a production environment. This is where the proper tooling becomes so essential.
The right tool stack has to provide at least three major functionalities – the ability to
- Update models quickly
- Utilize them in real-time decision-making
- Monitor results over time
Running machine learning models in a champion-challenger flow is another helpful practice to minimize the impact of a rouge model on your customer base.
So while you are thinking of starting a machine learning initiative in your company, start also thinking about how the output of your expensive data team can be utilized by your operations team most effectively.
How Effectiv helps to bring your own machine learning model to life
I built fraud machine learning models for a living for more than five years. I’ve seen some of the biggest global financial institutions succeeding and failing with their data projects. Everything I described above, I experienced first-hand. I saw how machine learning utilized right makes a profound difference to some of the world’s most advanced fraud and risk programs. But I also learned what all can go wrong and what a good, supporting risk management platform should provide to be an enabler, not a blocker.
This is why at Effectiv, we implemented all the best practices at the platform’s foundation right from the beginning. It starts with computing thousands of advanced fraud and risk features in real-time out of the box. The platform provides all data in a format that makes it easily usable for your data team to build models and draw the correct conclusions. And finally, we make it incredibly easy to bring your models to production, utilize it in your decision flow in real time and monitor its impact over time.
