Financial institutions today face numerous challenges--everything from intense regulatory pressures to upstarts coming at them from every direction with disruptive technology solutions--that threaten even their core businesses. According to a 2015 Capgemini report, 65% of affluent individuals will leave their current wealth management firm if an integrated channel experience is not provided. Per the 2016 Edelman Trust Barometer, from 2012 to 2016, global trust in financial services has risen by 8% to 51%--the largest increase of any industry surveyed--but it remains the lowest overall. In the U.S., 23.5 million households were unbanked or underbanked in 2015 (FDIC), while a staggering 2 billion adults globally were found to be unbanked in 2014 (World Bank). AI or artificial intelligence--what we like to call augmented intelligence--promises to make it possible for financial services organizations to respond to these imperatives, and with unexpected efficiency and agility.

Intelligent machines are becoming a ubiquitous and essential part of business operations. Algorithms trained by faster, smarter data now play a key role in determining customer demand, increasing customer trust and delivering unprecedented productivity and intelligence to the enterprise. But despite the success of some early adopters, most machine learning projects fail. Regardless of their size, location or industry, businesses have been struggling to fully unlock and realize the value of their information. In fact, a recent Information Value Index from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their data; 23 percent are unable to derive any real value at all.