We're about to enter the era where our all too familiar legacy systems will get a machine-assisted kick in the backside.

Credit checks have been around almost since credit has been - over 4,000 years. New companies are trying to solve an age-old problem: how can you do a credit check on someone with no credit history?

Current credit-scoring technology analyses about 50 data points - much more than any human could economically. Zest, brainchild of former Google CIO Douglas Merrill, analyses tens of thousands of data points at a time with blistering speed. They claim that it takes less than ten seconds to produce a machine-learning assisted credit rating on someone with zero credit history.

To train their A.I. & machine learning system they've partnered up with Baidu in China. Only 20% of the population in China has any known credit history. By discovering patterns in the data they gained from millions of transactions, they grew the approval rate of their lending business by 150% in two months.

To test for bias, Zest relies again on machine learning, by testing the system's results using the same system. It's just another step in the battle between human intuition and empirical machine-learned data - which one will prevail?