March 9, 2018
Worldwide spending on cognitive and AI systems has been forecast to reach $57.6 billion in 2021, according to the Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide from International Data Corporation (IDC). With many industries aggressively investing in cognitive and AI solutions, spending is expected to achieve a CAGR of 50.1% over the 2016-2021 forecast period. Worldwide spending on cognitive and AI systems were estimated to total $12.0 billion in 2017, an increase of 59.1% over 2016, IDC reported last September.
Artificial Intelligence: The Next Digital Frontier by MGI reveals that digital native companies made some of the most significant and earliest investments in AI, providing test cases for potential ROI in AI.
Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing. Click to ship cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment. – Artificial Intelligence: The Next Digital Frontier
At the end of 2017, IDC forecasted that retail and banking sectors will spend the most on AI systems. And for good reason: AI adoption and use survey by MGI found that out of 12 major industries, AI adopters with proactive strategies report current profit margins that are three to 15 percentage points higher than the industry average in most sectors, but they also expect this advantage to grow in the future, when they could expect their AI investment to mature and start paying substantial dividends. And industries with the highest margin delta are the financial services industry, retail, education, and healthcare.
The retail and banking industries are forecast to spend the most on cognitive and AI systems in 2017 with investments of $1.74 billion and $1.72 billion, respectively. The discrete manufacturing, healthcare, and process manufacturing industries are also forecast to spend more than $1 billion each this year. These five industries will continue to be the industries with the largest spending amounts throughout the five-year forecast and, by 2021, their combined investments will represent nearly 55% of all worldwide spending.
In addition to spending the most on cognitive and AI systems, retail will also deliver the fastest spending growth with a 2016-2021 CAGR of 58.8%. Six other industries will see CAGRs greater than 50% over the forecast period. – IDC, September 2017
According to MEDICI’s State of FinTech 2018 Report, key areas of AI/ML adoption impact in the financial services industry include:
Virtual assistants and chatbots
Capital markets – data analysis, algorithmic trading
Loyalty and rewards
Impact areas of AI/ML adoption are quite broad with some yet to be revamped, but certain uses have already seen real-life adoption and yielded significant results.
Source: State of FinTech 2018 Report, MEDICI
Out of the listed key impact areas, I’d like to focus on KYC/AML, as in 2018, money laundering, and financial fraud overall, are some of the sharpest problems across jurisdictions.
Preventing money laundering in the financial systems has high stakes. It aids in the prevention of terrorism, human trafficking, and narcotics distribution. What makes matters worse is that existing AML systems and processes have proven to be drastically inefficient. Proven by the fact that only 2-5% of global GDP, roughly $1-2 trillion annually, is made of money laundering transactions. – Anti-Money Laundering Solution Deep Dive, Ayasdi
Ayasdi emphasizes that what makes AML such a difficult problem to solve is that it involves complex data, detailed workflows, and significant human involvement.
The result is that the cost of compliance is increasing by 50% year-over-year and quickly becoming a drag on earnings at a critical time for financial institutions. The dimensionality of the data and the significant costs to support a robust AML process are amplified for large, geographically diverse financial institutions.
The company reports that as many as 95% of investigations do not result in a Suspicious Activity Report (SAR), which means that 95% of the effort of a large investigations team (anywhere from 500-5,000+ people) is not required.
To fight money laundering, in mid-2017, HSBC partnered with Ayasdi on automating some of the banks’ compliance processes in a bid to become more efficient. In a pilot of the startups’ technology, HSBC saw the number of investigations drop by 20% without reducing the number of cases referred for more scrutiny. HSBC learned its lesson the hard way – according to Reuters, in 2012, the bank agreed to pay a $1.92 billion in fines to US authorities for allowing itself to be used to launder drug money out of Mexico and other compliance lapses.
HSBC is not the only bank to be forced into the adoption of advanced technologies by own mistakes and hefty fines. In 2017, MAS has slapped UOB and Credit Suisse with fines of S$900,000 and S$700,000 respectively for breaches of MAS Notice 626 – Prevention of Money Laundering and Countering the Financing of Terrorism, The Straits Times reported. MAS has directed the banks to appoint independent parties to assess and confirm to MAS that rectification measures have been effectively implemented. Earlier the same year, Deutsche Bank was also fined $204 million by FCA for serious AML control failings.
Ravi Narayanan, Country Head – Branch Banking and Retail Trade FX Business, HDFC, fairly stated that AI will be the most defining technology for the banking industry.
I am most excited about AI. It is the most defining technology that’ll impact business, Narayanan said.
Given the variety of use cases that financial institutions have found for the advanced technology and undebatable benefits for organizations and customers, Narayanan is certainly not overestimating the value of AI in finances. In 2018, the BFSI sector (among others) will run from pilots to beneficial partnerships or strategic acquisitions, revamping existing business models and associated efficiency.