Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
Data science is the big draw in business schools
1012 days ago
7 Effective Methods for Fitting a Liner
1022 days ago
3 Thoughts on Why Deep Learning Works So Well
1022 days ago
3 million at risk from the rise of robots
1022 days ago
Top 10 Hot Artificial Intelligence (AI) Technologies
Banks Eager For Artificial Intelligence, But Slow To Adopt
Facebook, Google, Microsoft and Baidu spent at least $8.5 billion beefing up their AI talent. Amazon spends $228 million a year just to find people to run Alexa and related machine learning initiatives. Even small to medium businesses in every sector - from fast food chains and personal fitness centers - are reaping the business benefits of technologies like natural language processing, image recognition, and machine learning.
Yet only a few outliers in the banking sector, such as Capital One, have been able to ship AI products as quickly as their counterparts in Silicon Valley. While many financial institutions have publicly announced ambitious plans to integrate artificial intelligence and machine learning, customers are still waiting months later for these proposed products and services.
So why are banks - who are typically the most capable and tech-intensive players in the business world - acting like Luddites with AI? And how can AI entrepreneurs and developers building products for the industry nail their pitches and drive home deals?
Financial Institutions Are Complex Beasts
To be fair, banks have employed AI - at least in rudimentary forms - for decades. Computer automation has been used by the financial industry for back office and customer-facing operations since the 1960s. AI investments picked up in the 1980s in the form of expert systems.
Despite this strong IT heritage, many banks lack the agility to fundamentally transform their business with modern artificial intelligence. A joint study by National Business Research Institute and Narrative Science revealed that traditional financial institutions are still in the early stages of AI adoption, with a mere 32 percent of surveyed respondents confirming use of recommendation engines, predictive analytics, voice recognition and allied technologies.
For AI enablers in finance such as Kasisto, Voyager Labs, IBM Watson Cybersecurity, and Amazon's Alexa platform, the underserved market presents vast opportunities for engagement, but only if they can creatively overcome the industry's daunting challenges. 12% of financial institutions surveyed that weren't already using AI blamed the technology for being new, untested, and risky. Other firms cited "siloed data sets, regulatory compliance, fear of failure, and unclear internal ownership of emerging technologies" as main factors thwarting innovation. A similar study by PWC showed that two in every three financial services firms in the US were hindered in AI adoption by "operations, regulations, and limitations in budget or resources."
Dror Oren, Chief Product Officer and Co-Founder at Kasisto, reveals the many challenges of becoming enterprise-ready for banks. "Most big technology providers are cloud based. To be adopted by banks, you have to support on-premise deployment where banks' internal servers run your software." Additionally Oren explains that banks perform rigorous "penetration testing", also known as a "pen test", where security experts employ a range of blackhat strategies to attack and break any new technology being evaluated.
Disaster recovery is also critical and must be performed on premise. While most smaller AI companies rely on the cloud, banks require "hot-hot" recovery where downtimes only last a few seconds at most. The data-hungry nature of AI solutions, which often need to capture and mine vast volumes of consumer information and input, also creates challenges with compliance.
Overcoming The Challenges Of Selling AI To Banks
IDC predicts worldwide revenues from the adoption of cognitive systems across industries will experience a near six-fold increase from $8 billion in 2016 to over $47 billion in 2020, with retail and banking leading the charge. Meanwhile, the vast majority of banking executives believe that AI will become the "primary" channel through which banks and their customers will interact within the next three years.
The vendors who know how to sell into the market will invariably win. Kasisto, an SRI spinoff, is the leading conversational AI platform for financial services. Chief Product Officer Dror Oren explains that the technology leverages a hybrid approach, using both statistical classifiers and grammar-based classifiers, to enable consumers to have flexible, contextual conversations about their banking needs. Kasisto's platform also comes pre-loaded with banking-specific domain knowledge, including account management, payments, and credit card offers.
As one hedge against internal inertia, large companies across industries have started accelerators and incubator programs to attract the most promising startups. Kasisto participated in both Mastercard's Start Path and Wells Fargo's Startup Accelerator, enabling the company to develop critical relationships necessary for future sales.
According to Oren, there's a "general skepticism around AI" where bank executives often complain they haven't seen enough proof points. Overcoming this challenge requires finding the right visionary at a company who can become a champion for AI adoption. Often this champion is the digital lead for the internal products that will be first to integrate AI, such as a mobile app or a call center. But this is only step one. "Selling is always a cross-bank initiative," warns Oren, "You need to make sure everything is aligned with business drivers and technical complexities."
Singaporean bank DBS had the vision to launch digibank, India's first mobile-only bank. Being paperless and branchless, digibank had to rely on emerging technologies like conversational AI to succeed and proactively reached out to Kasisto. "Piyush Gupta, CEO of DBS, is a visionary of lifestyle banking," Oren beams. "He built digibank with one-fifth of the cost of a regular retail bank." Since adopting Kasisto technology and launching in mid-2016, digibank is able to contain 82% of customer inquiries with bots.
Visionaries like Gupta are rare, so Oren also recommends picking the right prospective customers and giving them self-service solutions. Banks that traditionally move fast, have clean and augmented data, and secure gateways to access that data are far more likely to adopt AI quickly. Once adopted, AI technologies still need to evolve and train, so Kasisto has built a sophisticated Annotation Tool for "AI trainers" inside of banks to easily identify and fix errors.
"Some banks just want to hand off responsibility to the vendor," Oren points out, "but our approach is to empower the customer with self-service tools. We don't want to be professional services." Read More