At Feuji, we are able to make our clients successful in increasing collections, enhancing operational efficiencies, saving time, preventing data leakages, and improving security and surveillance through our AI/ML solutions.
Based on our experience, here are some common apprehensions about AI/ML and our take on them.
- ML engineering is mostly suitable for complex problems like vision and comprehension
- It is true that ML technologies are very successful in solving such complex problems with thousands (or more) features. However, ML is very useful in solving even (so called) mundane business problems.
- Building ML solutions is very expensive
- It is true that solving complex comprehension problems does require a lot of time and resources. However, the decreasing costs of processing power and the increasing availability of open-source software enable us to build several real-world solutions at low cost.
- ML models need a lot of data
- We were able to build very good models by using only about 6 months’ historical data of a few tens of thousands of customers. In fact, more customer data points are only useful if their behavior is both unique and common to impact the model. After a threshold, that is not likely. Similarly, more history may not be useful if the customer behavior keeps changing and is not seasonally repetitive.
- Statistical models are better
- It is entirely possible to build extremely good statistical models. However, building these tend to be very resource intensive and on the number of features (influencers). Moreover, if the customer behavior changes significantly due to major external events, (e.g. war or recession), the ML models tend to automatically adjust whereas the statistical models may need to be rebuilt.