“In God we trust, all others must bring data.” – W. Edwards Deming
Genesis
The “data economy” has been underway for a couple of decades now, where data has been recognized as an asset vital to the business. Enterprises over time have developed robust processes, policies, organizations, and platforms to manage, monitor, and measure the quality, relevancy and availability of data for business needs at every step of a data life cycle.
In the modern era, the “Economics of Artificial Intelligence (AI)” is what revolutionizes business today. AI is perhaps best applied when it replaces human intelligence. Moreover, nowhere is it more relevant than in the realm of business process outsourcing (BPO) that thrives on data and analytics operations.
The initial model relied on “labor-arbitrage” using cheaper knowledge workers globally to execute data and analytics workstreams. In the next decade, it was optimized by designing “process arbitrage” that would optimize processes, create robotic process automation (RPA), and employ disciplined data governance frameworks, to extract further efficiencies. Today, with the smart use of cognitive sciences, the pragmatic possibilities to eliminate humans from the business process chain are endless, thereby ushering in the era of “decision arbitrage.” AI can be leveraged in many ways, for example, in self-corrected data harmonization, process optimization through bionic humans, process elimination through autonomous decision-making, and human displacement with cognitive applications, and many more.
Cost-inversion
While traditional process automation and governance models can promise 7-8 percent improvement in Service-Level-Agreements (SLA), a cognitive data operation has the potential to free up 60-70 percent of the human cost. It frees up capital by marshaling a strategic cost-inversion in business spend on operations, which in turn can be used to develop disruptive models and generate unprecedented competitiveness. Thus with AI, humans can potentially abrogate operational decision-making to cognitive machines while they focus on developing new business models and capturing new markets.
Re-Imagining Business Operations with Applied AI
While there are many business operations that are data-centric or analytics-driven can be re-structured using AI, here are a few examples that are applicable using current advances in technology
Smarter Data Governance
Empowering thinking machines to make decisions on behalf of humans, thereby eliminating the roles of decision-makers, such as stewards, from the data governance process, could significantly improve the ROA (return-on-asset) for data. Freeing up that capital improves the quality of revenue for any business, without losing the value of its key loss-leading asset: data.
A harness built with AI capabilities can apply cognitive science to tasks such as automated record corrections, Data quality rule discovery and execution from data patterns, outlier detection and corrections, best data recommendations, automated data catalog discovery, lineage discovery, and a slew of other human tasks that can be abrogated to robots.
The Cloud-Data Angle
By 2025 49% of the world’s stored data will reside in public cloud environments. (IDC DataAge, 2025). As the industries mature to use the ecosystem of a connected infrastructure with cloud-native designs and platforms, the business that can harness the power of this connected, collaborative ecosystem of data, will have a clear competitive advantage.
Using core AI design patterns, solutions related to data privacy, data obfuscation, reputation management, malicious crowd wisdom elimination, signal source validation and corroboration, data provenance, data breach and leakage prevention, and much more, would become the mainstay of companies that disrupt the status quo.
The Business of Trade-Finance
The cost of trade-finance is prohibitively high, leading to three major players, HSBC, Citi and Standard Chartered bank dominating the market. The high cost is due to manual paper based document processing and individualized complex custom rules checking. Automating this business with STP (straight through processing) can take out significant percentages of the cost, thus making it nimble for disruption and monopoly fracture
Traditional rules processing and paper digitization relies on humans defining logic, code, corrections and reconciliation, which works when the variables are limited and can be generalized. However, in trade finance, the complexity, volume and individuality of the process make it conducive to AI. It can be emulated by a learning platform that needs minimum human input.
Know Your Customer
While the KYC process is an important compliance requirement during the onboarding process, current methods used by businesses are inefficient, time consuming, and error-prone. AI can help drastically simplify the onboarding process in several ways. One, it can help automate the process of document verification and image quality checks. It can also detect fraudulent transactions and reduce the possibility of fraud. It can also allow for simpler digitization of physical documents through tools such as (Optical Character Recognition) OCR that extract the necessary information.
Digitizing Paper
Despite huge advances in technology, a number of business functions still rely on paper and manual validation for record keeping, liability, controls and disparity across channels. Turning paper to digital records, manual process to autonomous functions, security and traceability to Digital Vault, would slash operational costs, freeing up valuable capital to disrupt market-making models.
The disciplines of AI such as deep learning algorithms enhance the accuracy of OCR phenomenally, reputation systems create “online-notary” to validate documents, behavior recognition creates smart access to Digital Vault and image, voice and video processing creates bio-metric authentications that obviate the need for “signatures.”
New Normal
Using the Economics of AI, the reimagined landscape of “data operations” creates a higher operational value for business. Companies that can innovate out of business processes that were developed based on the .com revolution, and re-align human involvement in decision-making activities by abrogating those responsibilities to sentient, cognitive and autonomous bots, would lead the industries of the future.
About the Author
Gary Bhattacharjee heads up the Global Practice for AI at Infosys, enabling businesses with AI-led solutions. Gary started at IBM Australia, leading product development for CoreBank©. At Citi, he led the development of the Corporate Banking platform. Leading Financial Services Consulting at HP, he developed various strategic solutions for the industry. At Morgan Stanley, he led Strategy and Analytics for Wealth Management, achieving business goals through AI. He recently cofounded a FinTech startup where he built an autonomous platform to digitize manual paper-based operations of Trade Finance, with the use of machine learning and block-chain. Gary graduated from the Indian Institute of Technology (IIT) with a Bachelor’s in Electronics. He holds a patent on Management of Data via Cooperative Method and System, a wiki-based approach for managing structured data.
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