In his excellent book “Surviving A.I.”, author Calum Chace breaks down the origin story of the Artificial Intelligence ecosystem. The term Artificial Intelligence was first coined in the summer of 1956, by scientist John McCarthy at a month-long conference at Dartmouth College, the foundational event in the genesis of the science.
For the uninitiated, A.I. is a branch of computer science whose stated goal is to create “intelligent machines”, capable of mimicking the human decision-making process across any given range of endeavors. IBM’s Deep Blue computer, which beat Russian chess master Garry Kasparov in 1997, is one of the most famous demonstrations of the early iteration of AI.
Coming into this century, the next phase of A.I. came to the fore: machine learning. In ML, machines “learn” by themselves via the ingesting of large datasets. This allows them to improve with experience. IBM’s Watson beat human players on Jeopardy in 2011 utilizing ML, by being able to crunch 200 million pages of information that had been fed into it during its training. Upon losing, human contestant Ken Jennings famously quipped, “I for one welcome our new robot overlords.” The most potent phase of A.I.’s evolution, illustrated by Google’s AlphaGo beating Go master Lee Sedol, is deep learning. Deep learning is where multilayered neural networks are exposed to vast hordes of data, training machines to solve any problem which requires “thought”.
A.I. and leveraging machine learning to turn large sets of data into insight is arguably the greatest technological opportunity of all time. For the majority of us, the most obvious demonstration of A.I. sits in our pockets – our smartphones. Outside of smartphones, however, right now A.I. is capable of some staggering feats.
Deep learning neural networks – which are created to mimic the neural connections in our brains and are capable of learning by sucking in huge volumes of data – can execute speech and image recognition at levels higher than us. They can also compose and play music, conduct medical diagnoses, get you off a parking ticket.
The range of A.I.’s application stretches beyond our humble planet, too. In a speech at WIRED2016, a Swiss scientist and “Father of Deep Learning” Jürgen Schmidhuber stated that by 2050 “A.I.’s will colonize and transform the entire cosmos,” with trillions of self-replicating robot factories dotted on the asteroid belt. A “few million years later,” he continued “A.I. will colonize the galaxy”.
PwC ranks A.I. as the biggest commercial opportunity in today’s world, estimating it’ll add $15.7 trillion to the global economy ($6.6 trillion in labor productivity and $9.1 trillion in increased consumer demand), by 2030. To put that into some context, that’s equivalent to:
On the macro front, China aims to lead the world in the field of A.I. by 2030 (and they’re off to a prodigious start). On the micro-level, for the company that leads the AI cloud (democratizing AI as pay-per-use services), it will become the most powerful company in history.
Accenture estimates that the Financial Services industry will grow to $4.6 trillion by 2035, $1.2 trillion more than their baseline calculations point to in its absence. While crypto dominated the headlines in the financial space last year, FinTech companies were quietly infiltrating the industry in less flashy ways. This ranged from fundamentally moving the needle with regards to how we invest, borrow and save, how banks manage risk and regulation as well as how hedge funds scrutinize data and execute trades.
To give you an idea here are 9 main categories that A.I.-first FinTech companies are targeting:
A.I. Application: A.I. chatbots and mobile app assistants for monitoring personal finances.
Notable Names: Active.Ai, Cleo, digit, HomeBot, Kasisto, MoneyLion, Penny, Personetics, and Trim
A.I. Application: Improving basic business accounting, including expense reporting.
Notable Names: AppZen, Fyle, HighRadius Rivana, NetChain2, and Zeitgold
A.I. Application: Robust credit scoring and lending applications.
Notable Names: Affirm, HighRadius Rivana, Upstart, and ZestFinance.
A.I. Application: To improve creditor collection of outstanding debt via personalized and automated communication.
Notable Names: CollectAI, HighRadius Rivana, TrueAccord
A.I. Application: General purpose semantic and natural language applications, as well as broadly applied predictive analytics.
Notable Names: Ayasdi, HighRadius Rivana, Kensho Technologies, and Opera.
A.I. Application: Quoting and insuring.
Notable Names: Captricity, Lemonade, riskgenius, Shift Technology, Tractable, Understory, Zendrive
A.I. Application: Researching and quantifying sentiment efficiently.
Notable Names: acuity, Alphasense, Dataminr, Feedstock, indico, iSentium, Orbital Insight, and Signal
A.I. Application: Algorithmic trading and investment strategies or tools.
Notable Names: Algoriz, Alpine Data, Clone Algo, domeyard, Numerai, Sentient Technologies, SigOpt, Trumid, and Wealthfront.
A.I. Application: Fraud and abnormal financial behavior detection, and/or to improve regulatory compliance and workflows.
Notable Names: CheckRecipient (Tessian), ComplyAdvantage, DataRobot, Digital Reasoning, Dimebox, Fortia, Fraugster, PredicSis, Sift Science, Skytree, Trifacta, and WorkFusion.
Artificial Intelligence is going to infiltrate every facet of business, and finance as we know it is going to be unrecognizable in short order. While our “robot overlords” haven’t taken over just yet, the revolution is gathering a staggering pace.
Original Source: LinkedIn
HighRadius Autonomous Receivables Software Platform is the world’s only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Autonomous Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway – covering the entire gamut of credit-to-cash.