August 1, 2017
Machine Learning (ML) technology can help us draw important insights from data, but it is imperative to recognize a model is not an end in and of itself. Based on BFA’s experiences engaging with early-stage partners in emerging markets, such as Catalyst Fund investees, we have seen the consequences of rushing into machine learning without a clear understanding of the underlying data.
As a business, misreading this data can cause you to chase errant hypotheses around the needs of your core set of customers, which in extreme cases, can cost you everything. To this point, we recommend here that FinTech startups and other financial institutions focus first on producing and refining this data as the fuel to get an insights engine running, before exploring increasingly sophisticated models.
Artificial intelligence (AI) and machine learning are some of the hottest buzzwords of today. With the increased exposure, there is an associated growth in the number of businesses and individuals eager to incorporate these exciting tools into the fabric of their operations.
In this air of excitement and hype, innovators and entrepreneurs seeking to develop an AI/ML strategy may raise a whole host questions: