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Taktile makes it easier to leverage machine learning in the financial industry

·2-min read

Meet Taktile, a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. But Taktile wants to differentiate itself from competitors by making it way easier to get started and switch to AI-powered models.

A few years ago, when you could read "machine learning" and "artificial intelligence" in every single pitch deck, some startups chose to focus on the financial industry in particular. It makes sense as banks and insurance companies gather a ton of data and know a lot of information about their customers. They could use that data to train new models and roll out machine learning applications.

New fintech companies put together their own in-house data science team and started working on machine learning for their own products. Companies like Younited Credit and October use predictive risk tools to make better lending decisions. They have developed their own models and they can see that their models work well when they run them on past data.

But what about legacy players in the financial industry? A few startups have worked on products that can be integrated in existing banking infrastructure. You can use artificial intelligence to identify fraudulent transactions, predict creditworthiness, detect fraud in insurance claims, etc.

Some of them have been thriving, such as Shift Technology with a focus on insurance in particular. But a lot of startups build proof of concepts and stop there. There’s no meaningful, long-term business contract down the road.

Taktile wants to overcome that obstacle by building a machine learning product that is easy to adopt. It has raised a $4.7 million seed round led by Index Ventures with Y Combinator, firstminute Capital, Plug and Play Ventures and several business angels also participating.

The product works with both off-the-shelf models and customer-built models. Customers can customize those models depending on their needs. Models are deployed and maintained by Taktile’s engine. It can run in a customer’s cloud environment or as a SaaS application.

After that, you can leverage Taktile’s insights using API calls. It works pretty much like integrating any third-party service in your product. The company tried to provide as much transparency as possible with explanations for each automated decision and detailed logs. As for data sources, Taktile supports data warehouses and data lakes as well as ERP and CRM systems.

It’s still early days for the startup, and it’s going to be interesting to see whether Taktile’s vision pans out. But the company has already managed to convince some experienced backers. So let’s keep an eye on them.
