Artificial Intelligent (AI) “Assistants”, or “Bots” are taken to the ‘next level’ when the assistant becomes a proactive entitybased on the input from human intelligent experts that grows with machine learning.
Even the implication of an ‘Assistant’ v. ‘Life Partner’ implies a greater degree of dynamic, and proactive interaction. The cross over to becoming ‘Life Partner’ is when we go ‘above and beyond’ to help our partners succeed, or even survive the day to day.
Once we experience our current [digital, mobile] ‘assistants’ positively influencing our lives in a more intelligent, proactive manner, an emotional bond ‘grows’, and the investment in this technology will also expand.
Practical Applications Range:
Alcoholics Anonymous Coach , Mentor – enabling the human partner to overcome temporary weakness. Knowledge, and “triggers” need to be incorporated into the AI ‘Partner’; “Location / Proximity” reminder if person enters a shopping area that has a liquor store. [AI] “Partner” help “talk down”
Understanding ‘data points’ from multiple sources, such as alarms, and calendar events, to derive ‘knowledge’, and create an actionable trigger.
e.g. “Did you remember to take your medicine?” unprompted; “There is a new article in N periodical, that pertains to your medicine. Would you like to read it?”
e.g. 2 unprompted, “Weather calls for N inches of Snow. Did you remember to service your Snow Blower this season?”
FinTech – while in department store XYZ looking to purchase Y over a certain amount, unprompted “Your credit score indicates you are ‘most likely’ eligible to ‘sign up’ for a store credit card, and get N percentage off your first purchase” Multiple input sources used to achieve a potential sales opportunity.
IBM has a cognitive cloud of AI solutions leveraging IBM’s Watson. Most/All of the 18 web applications they have hosted (with source) are driven by human interactive triggers, as with the “Natural Language Classifier”, which helps build a question-and-answer repository.
There are four bits that need to occur to accelerate adoption of the ‘AI Life Partner’:
Knowledge Experts, or Subject Matter Experts (SME) need to be able to “pass on” their knowledge to build repositories. IBM Watson Natural Language Classifier may be used.
The integration of this knowledge into an AI medium, such as a ‘Digital Assistant’ needs to occur with corresponding ‘triggers’
Our current AI ‘Assistants’ need to become [more] proactive as they integrate into our ‘digital’ lives, such as going beyond the setting of an alarm clock, hands free calling, or checking the sports score. Our [AI] “Life Partner” needs to ‘act’ like buddy and fan of ‘our’ sports team. Without prompting, proactively serve up knowledge [based on correlated, multiple sources], and/or take [acceptable] actions.
E.g. FinTech – “Ourschedule is open tonight, and there are great seats available, Section N, Seat A for ABC dollars on Stubhub. Shall I make the purchase?”
Partner with vendors to drive FinTech business rules.
Take ‘advantage’ of more knowledge sources, such as the applications we use that collect our data. Use multiple knowledge sources in concert, enabling the AI to correlate data and propose ‘complex’ rules of interaction.
Our AI ‘Life Partners’ may grow in knowledge, and mature the relationship between man and machine. Incorporating derived rules leveraging machine learning, without input of a human expert, will come with risk and reward.
FinTech refers to new solutions which demonstrate an incremental or radical / disruptive innovation development of applications, processes, products or business models in the financial services industry. These solutions can be differentiated in at least five areas.
First, the banking or insurance sector are distinguished as potential business sectors. Solutions for the insurance industry are often more specifically named “InsurTech”.
Second, the solutions differ with regard to their supported business processes such as financial information, payments, investments, financing, advisory and cross-process support. An example is mobile payment solutions.
Third, the targeted customer segment distinguishes between retail, private and corporate banking as well as life and non-life insurance. An example are telematics-based insurances that calculate the fees based on customer behaviour in the area of non-life insurances.
Fourth, the interaction form can either be business-to-business (B2B), business-to-consumer (B2C) or consumer-to-consumer (C2C). An example are social trading solutions for C2C.
Fifth, the solutions vary with regard to their market position. Some for example provide complementary services such as personal finance management systems, others focus on competitive solutions such as e.g. peer-to-peer lending.
Global investment in financial technology increased more than twelvefold from $930 million in 2008 to more than $12 billion in 2014
Throughout my career, I’ve worked with several financial services teams to engineer, test, and deploy solutions. Here is a brief list of the FinTech solutions I helped construct, test, and deploy:
3K Global Investment Bankers – proprietary CRM platform, including Business Analytics, Business Objects Universe.
Equity Research platform, crafted based on business expertise.
Custom UI for research analysts, enabled the analysts to create their research, and push into the workflow.
Based on a set of rules, ‘locked down’ part of the report would “Build Discloses” , e.g. analyst holds 10% of co.
Custom Documentum workflow would route research to the distribution channels; or direct research to legal review.
(Multiple Financial Org.) Data Warehouse middleware solutions to assist organizations in managing, and monitoring usage of their DW.
Global Derivatives firm, migration of mainframe system to C# client / Server platform
Investment Bankers and Equity Capital Markets (ECMG) build trading platform so teams may collaborate on Deals/Trades.
Global Asset Management Firm: On boarding and Fund management solutions, custom UI and workflows in SharePoint
A “Transaction Management Solution” targets a mixture of FinTech services, primarily “Payments” Processing.
Target State Capabilities of a Transaction Management Solution:
Fraud Detection: The ability to identify and prevent fraud exists within many levels of the transaction from facilitators of EFT to credit monitoring and scoring agencies. Every touch point of a transaction has its own perspective of possible fraud, and must be evaluated to the extent it can be.
Business experts (SMEs) and technologists continue to expand the practical applications of Artificial Intelligence (AI) every day. Although extensive AI fraud detection applications exists today incorporating human populated Rules Engines, and AI Machine learning (independent rule creation).
Consumer “Financial Insurance” Products
Observing a business, end to end transaction may provide visibility into areas of transaction risk. Process and/or technology may be adopted / augmented to minimize the risk.
E.g. eBay auction process has a risk regarding the changing hands of currency and merchandise. A “delayed payment”, holding funds until the merchandise has been exchanged minimized the risk, implemented using PayPal.
In product lifecycle of Discovery, Development, and Delivery phases, converting concept to product.
For quite some time companies have attempted to tread in this space with mixed results, either through acquisition or build out of their existing platforms. There seems to be significant opportunities within the services, software and infrastructure areas. It will be interesting to see how it all plays out.
Inhibitors to enclosing a transaction within an end to end Transaction Management Solutions (TMS):
Higher level of risk (e.g. business, regulatory) expanding out service offerings
Stretching too thin, beyond core vision, and lose sight of vision.
Transforming tech company to hybrid financial services
Automation, streamlining of processes, may derive efficiencies may lead to reduction in staff / workforce
Multiple platforms performing functions provides redundant capabilities, reduced risk, and more consumer choices