Tag Archives: AI

AI Whispering Digital Co-Counsel for Any Litigation

Are you adequately prepared for your next litigation?  Going into court with an army of Co-Counsel making you feel more confident, more prepared?  Make sure you bring along the AI Whispering Digital Co-Counsel.  Co-Counsel that doesn’t break a sweat, get nervous, and is always prepared.  He even takes the opportunity to learn while on the job, machine learning.

The whispering digital agent for advising litigators “just-in-time” rebuttal citing historical precedence, for example.  Digital Co-Counsel analyzes the dialog within the courtroom to identify ‘goals’, the intent of the conversation(s).  The Digital Co-Counsel identifies the current workflow, which may be identified as Cross or Direct examination, Opening Statement, and Closing Argument.

Realtime observation of a court case and advice based on:
  • Observed dialog interactions between all parties involved in the case, such as opposing counsel,  witnesses, subject matter experts, may trigger “guidance” from the Digital Co-Counsel based on a compound of utterances, and identified workflow.
  • Court case evidence submitted may be digitized, and analyzed based on a [predetermined]combination of identified attributes of submitted evidence.  This evidence, in turn, may be rebutted, by counter arguments, alternate ‘perspectives’ or present “evidence” to rebut
  • The introduction of ‘bias’ toward the opposing council.**

Implementation of the Digital Co-Council may be through a Smartphone application, and use a bluetooth throughout the case.

My opinions are my own, and do not necessarily reflect my employer’s viewpoint.

AI Email Workflows Eliminate Need for Manual Email Responses

When i read the article “How to use Gmail templates to answer emails faster.”  I thought wow, what an 1990s throwback!

Microsoft Outlook has had an AI Email Rules Engine for years and years. From using a simple Wizard to an advanced construction rules user interface. Oh the things you can do. Based on a wide away of ‘out of the box’ identifiers to highly customizable conditions, MS Outlook may take action on the client side of the email transaction or on the server side. What types of actions? All kinds of transactions ranging from ‘out of the box’ to a high degree of customization. And yes, Outlook (in conjunction with MS Exchange) may be identified as a digital asset management (DAM) tool.

Email comes into an inbox, based on “from”, “subject”, contents of email, and a long list of attributes, MS Outlook [optionally with MS Exchange], for example, may push the Email and any attached content, to a server folder, perhaps to Amazon AWS S3, or as simple as an MS Exchange folder.

Then, optionally a ‘backend’ workflow may be triggered, for example, with the use of Microsoft Flow. Where you go from there has almost infinite potential.

Analogously, Google Gmail’s new Inbox UI uses categorization based on ‘some set’ of rules is not something new to the industry, but now Google has the ability. For example, “Group By” through Google’s new Inbox, could be a huge timesaver. Enabling the user to perform actions across predefined email categories, such as delete all “promotional” emails, could be extremely successful. However, I’ve not yet seen the AI rules that identify particular emails as “promotional” verses “financial”. Google is implying these ‘out of the box’ email categories, and the way users interact, take action, are extremely similar per category.

Google may continue to follow in the footsteps of Microsoft, possibly adding the initiation of workflows based on predetermined criteria. Maybe Google will expose its AI (Email) Rules Engine for users to customize their workflows, just as Microsoft did so many years ago.

Although Microsoft’s Outlook (and Exchange) may have been seen as a Digital Asset Management (DAM) tool in the past, the user’s email Inbox folder size could have been identified as one of the few sole inhibitors.  Workaround, of course, using service accounts with vastly higher folder quota / size.

My opinions do not reflect that of my employer.

AI Digital Assistant verse Search Engines

Aren’t AI Digital Assistants just like Search Engines? They both try to recognize your question or human utterance as best as possible to serve up your requested content. E.g.classic FAQ. The difference in the FAQ use case is the proprietary information from the company hosting the digital assistant may not be available on the internet.

Another difference between the Digital Assistant and a Search Engine is the ability of the Digital Assistant to ‘guide’ a person through a series of questions, enabling elaboration, to provide the user a more precise answer.

The Digital Assistant may use an interactive dialog to guide the user through a process, and not just supply the ‘most correct’ responses. Many people have flocked to YouTube for instructional type of interactive medium. When multiple workflow paths can be followed, the Digital Assistant has the upper hand.

The Digital Assistant has the capability of interfacing with 3rd parties (E.g. data stores with API access). For example, there may be a Digital Assistant hosted by Medical Insurance Co that has the ability to not only check the status of a claim, but also send correspondence to a medical practitioner on your behalf. A huge pain to call the insurance company, then the Dr office, then the insurance company again. Even the HIPPA release could be authenticated in real time, in line during the chat.  A digital assistant may be able to create a chat session with multiple participants.

Digital Assistants overruling capabilities over Search Engines are the ability to ‘escalate’ at any time during the Digital Assistant interaction. People are then queued for the next available human agent.

There have been attempts in the past, such as Ask.com (originally known as Ask Jeeves) is a question answering-focused e-business.  Google Questions and Answers (Google Otvety, Google Ответы) was a free knowledge market offered by Google that allowed users to collaboratively find good answers, through the web, to their questions (also referred as Google Knowledge Search).

My opinions are my own, and do not reflect my employer’s viewpoint.

Twitter Trolls caused Salesforce to Walk Away from Deal? Google reCAPTCHA to the Rescue!?

According to CNBC’s “Mad Money” host Jim Cramer, Salesforce was turned off by a more fundamental problem that’s been hurting Twitter for years: trolls.

“What’s happened is, a lot of the bidders are looking at people with lots of followers and seeing the hatred,” Cramer said on CNBC’s “Squawk on the Street,” citing a recent conversation with Benioff. “I know that the haters reduce the value of the company…I know that Salesforce was very concerned about this notion.”

…Twitter’s troll problem isn’t anything new if you’ve been following the company for a while.”

Source: Twitter trolls caused Salesforce to walk away from deal – Business Insider

Anyone with a few neurons will recognize that bots on Twitter are a huge turnoff in some cases.  I like periodic famous quotes as much as the next person, but it seems like bots have invaded Twitter for a long time, and becomes a detractor to using the platform.  The solution in fact is quite easy, reCAPTCHA.  a web application that determines if the user is a human and not a robot.  Twitter users should be required to use an integrated reCAPTCHA Twitter DM, and/or as a “pinned”reCAPTCHA tweet that sticks to the top of your feed,  once a calendar week, and go through the “I’m not a robot” quick and easy process.

Additionally, an AI rules engine may identify particular patterns of Bot activity, flag it, and force the user to go through the Human validation process within 24 hours.  If users try to ‘get around’ the Bot\Human identification process,  maybe by tweaking their tweets, Google may employ AI machine learning algorithms to feed the “Bot” AI rules engine patterns.

Every Twitter user identified as “Human” would have the picture of the “Vitruvian Man” by  Leonardo da Vinci miniaturized, and placed next to the “Verified Account” check mark.  Maybe there’s a fig leaf too.

In addition, the user MAY declare it IS a bot, and there are certainly valid reasons to utilize bots.  Instead of the “Man” icon, Twitter may allow users to pick the bot icon, including the character from the TV show “Futurama”, Bender miniaturized.  Twitter could collect additional information on Bots for enhanced user experience, e.g. categories and subcategories

reCAPTCHA is owned by Google, so maybe, in some far out distant universe, a Doppelgänger Google would buy Twitter, and either phase out or integrate G+ with Twitter.

If trolls/bots are such a huge issue, why hasn’t Twitter addressed it?  What is Google using to deal with the issue?

The prescribed method seems too easy and cheap to implement, so I must be missing something.  Politics maybe?  Twitter calling upon a rival, Google (G+) to help craft a solution?

Hey Siri, Ready for an Antitrust Lawsuit Against Apple? Guess Who’s Suing.

The AI personal assistant with the “most usage” spanning  connectivity across all smart devices, will be the anchor upon which users will gravitate to control their ‘automated’ lives.  An Amazon commercial just aired which depicted  a dad with his daughter, and the daughter was crying about her boyfriend who happened to be in the front yard yelling for her.  The dad says to Amazon’s Alexa, sprinklers on, and yes, the boyfriend got soaked.

What is so special about top spot for the AI Personal Assistant? Controlling the ‘funnel’ upon which all information is accessed, and actions are taken means the intelligent ability to:

  • Serve up content / information, which could then be mixed in with advertisements, or ‘intelligent suggestions’ based on historical data, i.e. machine learning.
  • Proactive, suggestive actions  may lead to sales of goods and services. e.g. AI Personal Assistant flags potential ‘buys’ from eBay based on user profiles.

Three main sources of AI Personal Assistant value add:

  • A portal to the “outside” world; E.g. If I need information, I wouldn’t “surf the web” I would ask Cortana to go “Research” XYZ;   in the Business Intelligence / data warehousing space, a business analyst may need to run a few queries in order to get the information they wanted.  In the same token, Microsoft Cortana may come back to you several times to ask “for your guidance”
  • An abstraction layer between the user and their apps;  The user need not ‘lift a finger’ to any app outside the Personal Assistant with noted exceptions like playing a game for you.
  • User Profiles derived from the first two points; I.e. data collection on everything from spending habits, or other day to day  rituals.

Proactive and chatty assistants may win the “Assistant of Choice” on all platforms.  Being proactive means collecting data more often then when it’s just you asking questions ADHOC.  Proactive AI Personal Assistants that are Geo Aware may may make “timely appropriate interruptions”(notifications) that may be based on time and location.  E.g. “Don’t forget milk” says Siri,  as your passing the grocery store.  Around the time I leave work Google maps tells me if I have traffic and my ETA.

It’s possible for the [non-native] AI Personal Assistant to become the ‘abstract’ layer on top of ANY mobile OS (iOS, Android), and is the funnel by which all actions / requests are triggered.

Microsoft Corona has an iOS app and widget, which is wrapped around the OS.  Tighter integration may be possible but not allowed by the iOS, the iPhone, and the Apple Co. Note: Google’s Allo does not provide an iOS widget at the time of this writing.

Antitrust violation by mobile smartphone maker Apple:  iOS must allow for the ‘substitution’ of a competitive AI Personal Assistant to be triggered in the same manner as the native Siri,  “press and hold home button” capability that launches the default packaged iOS assistant Siri.
Reminiscent of the Microsoft IE Browser / OS antitrust violations in the past.

Holding the iPhone Home button brings up Siri. There should be an OS setting to swap out which Assistant is to be used with the mobile OS as the default.  Today, the iPhone / iPad iOS only supports “Siri” under the Settings menu.

ANY AI Personal assistant should be allowed to replace the default OS Personal assistant from Amazon’s Alexa, Microsoft’s Cortana to any startup company with expertise and resources needed to build, and deploy a Personal Assistant solution.  Has Apple has taken steps to tightly couple Siri with it’s iOS?

AI Personal Assistant ‘Wish” list:

  • Interactive, Voice Menu Driven Dialog; The AI Personal Assistant should know what installed [mobile] apps exist, as well as their actionable, hierarchical taxonomy of feature / functions.   The Assistant should, for example, ask which application the user wants to use, and if not known by the user, the assistant should verbally / visually list the apps.  After the user selects the app, the Assistant should then provide a list of function choices for that application; e.g. “Press 1 for “Play Song”
    • The interactive voice menu should also provide a level of abstraction when available, e.g. User need not select the app, and just say “Create Reminder”.  There may be several applications on the Smartphone that do the same thing, such as Note Taking and Reminders.  In the OS Settings, under the soon to be NEW menu ‘ AI Personal Assistant’, a list of installed system applications compatible with this “AI Personal Assistant” service layer should be listed, and should be grouped by sets of categories defined by the Mobile OS.
  • Capability to interact with IoT using user defined workflows.  Hardware and software may exist in the Cloud.
  • Ever tighter integration with native as well as 3rd party apps, e.g. Google Allo and Google Keep.

Apple could already be making the changes as a natural course of their product evolution.  Even if the ‘big boys’ don’t want to stir up a hornet’s nest, all you need is VC and a few good programmers to pick a fight with Apple.

AI Personal Assistant Needs Remedial Guidance for their Users

Providing Intelligent ‘Code’ Completion

At this stage in the application platform growth and maturity of the AI Personal Assistant, there are many commands and options that common users cannot formulate due to a lack of knowledge and experience .

A key usability feature for many integrated development environments (IDE) are their capability to use “Intelligent Code Completion” to guide their programmers to produce correct, functional syntax. This feature also enables the programmer to be unburdened by the need to look up syntax for each command reference, saving significant time.  As the usage of the AI Personal Assistant grows, and their capabilities along with it, the amount of “command and parameters” knowledge required to use the AI Personal Assistant will also increase.

AI Leveraging Intelligent Command Completion

For each command parameter [level\tree], a drop down list may appear giving users a set of options to select for the next parameter. A delimiter such as a period(.) indicates to the AI Parser another set of command options must be presented to the person entering the command. These options are typically in the form of drop down lists concatenated to the right of the formulated commands.

AI Personal Assistant Language Syntax

Adding another AI parser on top of the existing syntax parser may allow commands like these to be executed:

  • Abstraction (e.g. no application specified)
    • Order.Food.Focacceria.List123
    • Order.Food.FavoriteItalianRestaurant.FavoriteLunchSpecial
  • Application Parser
    • Seamless.Order.Food.Focacceria.Large Pizza

These AI command examples uses a hierarchy of commands and parameters to perform the function. One of the above commands leverages one of my contacts, and a ‘List123’ object.  The ‘List123’ parameter may be a ‘note’ on my Smartphone that contains a list of food we would like to order. The command may place the order either through my contact’s email address, fax number, or calling the business main number and using AI Text to Speech functionality.

All personal data, such as Favorite Italian Restaurant,  and Favorite Lunch Special could be placed in the AI Personal Assistant ‘Settings’.  A group of settings may be listed as Key-Value pairs,  that may be considered short hand for conversations involving the AI Assistant.

A majority of users are most likely unsure of many of the options available within the AI Personal assistant command structure. Intelligent command [code] completion empowers users with visibility into the available commands, and parameters.

For those without a programming background, Intelligent “Command” Completion is slightly similar to the autocomplete in Google’s Search text box, predicting possible choices as the user types. In the case of the guidance provided by an AI Personal Assistant the user is guided to their desired command; however, the Google autocomplete requires some level or sense of the end result command. Intelligent code completion typically displays all possible commands in a drop down list next to the constructor period (.). In this case the user may have no knowledge of the next parameter without the drop down choice list.  An addition feature enables the AI Personal Assistant to hover over one of the commands\parameters to show a brief ‘help text’ popup.

Note, Microsoft’s Cortana AI assistant provides a text box in addition to speech input.  Adding another syntax parser could be allowed and enabled through the existing User Interface.  However, Siri seems to only have voice recognition input, and no text input.

Is Siri handling the iOS ‘Global Search’ requests ‘behind the scenes’?  If so, the textual parsing, i.e. the period(.) separator would work. Siri does provide some cursory guidance on what information the AI may be able to provide,  “Some things you can ask me:”

With only voice recognition input, use the Voice Driven Menu Navigation & Selection approach as described below.

Voice Driven, Menu Navigation and Selection

The current AI personal assistant, abstraction layer may be too abstract for some users.  The difference between these two commands:

  • Play The Rolling Stones song Sympathy for the Devil.
    • Has the benefit of natural language, and can handle simple tasks, like “Call Mom”
    • However, there may be many commands that can be performed by a multitude of installed platform applications.

Verse

  • Spotify.Song.Sympathy for the Devil
    • Enables the user to select the specific application they would like a task to be performed by.
  • Spotify Help
    • A voice driven menu will enable users to understand the capabilities of the AI Assistant.    Through the use of a voice interactive menu, users may ‘drill down’ to the action they desire to be performed. e.g. “Press # or say XYZ”
    • Optionally, the voice menu, depending upon the application, may have a customer service feature, and forward the interaction to the proper [calling or chat] queue.

Update – 9/11/16

  • I just installed Microsoft Cortana for iOS, and at a glance, the application has a leg up on the competition
    • The Help menu gives a fair number of examples by category.  Much better guidance that iOS / Siri 
    • The ability to enter\type or speak commands provides the needed flexibility for user input.
      • Some people are uncomfortable ‘talking’ to their Smartphones.  Awkward talking to a machine.
      • The ability to type in commands may alleviate voice command entry errors, speech to text translation.
      • Opportunity to expand the AI Syntax Parser to include ‘programmatic’ type commands allows the user a more granular command set,  e.g. “Intelligent Command Completion”.  As the capabilities of the platform grow, it will be a challenge to interface and maximize AI Personal Assistant capabilities.

Microsoft Flow – Platform Review

It looks like Microsoft created a generic workflow platform, product independent.

Microsoft has software solutions, like MS Outlook with an [email] rules engine built into Outlook.  SharePoint has a workflow solution within the Sharepoint Platform, typically governing the content flowing through it’s system.

Microsoft Flow is a different animal.  It seems like Microsoft has built a ‘generic’ rules engine for processing almost any event.  The Flow product:

  1. Start using the product from one of two areas:  a) “My Flows” where I may view existing and create new [work]flows. b) “Activity”, that shows “Notifications” and “Failures”
  2. Select “My Flows”, and the user may “Create [a workflow] from Blank”,  or “Browse Templates”.  MSFT existing set of templates were created by Microsoft, and also by a 3rd party implying a marketplace.
  3. Select “Create from Blank” and the user has a single drop down list of events, a culmination events across Internet products. There is an implication there could be any product, and event “made compatible” with MSFT Flows.
    1. The drop down list of events has a format of “Product – Event”.  As the list of products and events grow, we should see at least two separate drop down lists, one for products, and a sub list for the product specific events.
    2. Several Example Events Include:
      1. “Dropbox – When a file is created”
      2. “Facebook – When there is a new post to my timeline”
      3. “Project Online – When a new task is created”
      4. “RSS – When a feed item is published”
      5. “Salesforce – When an object is created”
    3. The list of products as well as there events may need a business analyst to rationalize the use cases.
  4. Once an Event is selected, event specific details may be required, e.g. Twitter account details, or OneDrive “watch” folder
  5. Next, a Condition may be added to this [work]flow,  and may be specific to the Event type, e.g. OneDrive File Type properties [contains] XYZ value.  There is also an “advanced mode” using a conditional scripting language.
  6. There is “IF YES” and “IF NO” logic, which then allows the user to select one [or more] actions to perform
    1. Several Action Examples Include:
      1. “Excel – Insert Rows”
      2. “FTP – Create File”
      3. “Google Drive – List files in folder”
      4. “Mail – Send email”
      5. “Push Notification – Send a push notification”
    2. Again, it seems like an eclectic bunch of Products, Actions, and Events strung together to have a system to POC.
  7. The Templates list, predefined set of workflows that may be of interest to anyone who does not want to start from scratch.   The UI provides several ways to filter, list, and search through templates.

Applicable to everyday life, from an individual home user, small business, to the enterprise.  At this stage the product seems in Beta at best, or more accurately, just after clickable prototype.  I ran into several errors trying to go through basic use cases, i.e. adding rules.

Despite the “Preview” launch, Microsoft has showed us the power in [work]flow processing regardless of the service platform provider, e.g.  Box, DropBox, Facebook, GitHub, Instagram, Salesforce, Twitter, Google, MailChimp, …

Microsoft may be the glue to combine service providers who may / expose their services to MSFT Flow functionality.

Create from Blank - Select Condition
Create from Blank – Select Condition

 

Create Rule from Template
Create Rule from Template
Create from Blank Rule Building UI
Create from Blank Rule Building UI

 

Update June 28th, 2016:

Opportunities for Event, Condition, Action Rules

  • Transcoding [cloud] Services
  • [IBM Watson] Cognitive APIs
    • e.g. Language:Translation; E.g.2. Visual Recognition;
  • WordPress – Create a Post
    • New text file dropped in specific folder on Box, DropBox, etc. being ‘monitored’ by MSFT flow [?] Additional code required by user for ‘polling’ capabilities
    • OR new text file attached, and emailed to specific email account folder ‘watched’ by MSFT Flow.
    • Event triggers – Automatic read of new text file
      • stylizing may occur if HTML coding used
    • Action – Post to a Blog
  • ‘ANY’ Event occurs, a custom message is sent using Skype for a single or group of Skype accounts;
    • On several ‘eligible’ events, such as “File Creation” into Box,  the file (or file shared URL) may be sent to the Skype account.
  • ‘ANY’ Event occurs, a custom mobile text message is sent to a single or group of phone numbers.
  • Event occurs for “File Creation” e.g. into Box; after passing a “Condition”, actions occur:
    • IBM Watson Cognitive API, Text to Speech, occurs, and the product of the action is placed in the same Box folder.
  • Action: Using Microsoft Edge (powered by MSN), in the “My news feed” tab, enable action to publish “Cards”, such as app notifications

Challenges \ Opportunities \ Unknowns

  • 3rd party companies existing, published [cloud; web service] APIs may not even need any modification to integrate with Microsoft Flow; however, business approval may be required to use the API in this manner,
  • It is unclear re: Flow Templates need to be created by the product owner, e.g. Telestream, or knowledgeable third party, following the Android, iOS, and/or MSFT Mobile Apps model.
  • It is unclear if the MSFT Flow app may be licensed individually in the cloud, within the 365 cloud suite, or offered for Home and\or Business?

Building AI Is Hard—So Facebook Is Building AI That Builds AI

“…companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art [AI] forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.”


This article represents a true picture of where we are today for the average consumer and producer of information, and the companies that repurpose information, e.g. in the form of advertisements.  
The advancement and current progress of Artificial Intelligence, Machine Learning, analogously paints a picture akin to the 1970s with computers that fill rooms, and accept punch cards as input.
Today’s consumers have mobile computing power that is on par to the whole rooms of the 1970s; however, “more compute power” in a tinier package may not be the path to AI sentience.  How AI algorithm models are computed might need to take an alternate approach.  
In a classical computation system, a bit would have to be in one state or the other. However quantum mechanics allows the qubit to be in a superposition of both states at the same time, a property which is fundamental to quantum computing.
The construction, and validation of Artificial Intelligence, Machine Learning, algorithm models should be engineered on a Quantum Computing framework.

AI Personal Assistants are “Life Partners”

Artificial Intelligent (AI)  “Assistants”, or “Bots” are taken to the ‘next level’ when the assistant becomes a proactive entity based 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’:

  1. 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.
  2. The integration of this knowledge into an AI medium, such as a ‘Digital Assistant’ needs to occur with corresponding ‘triggers’ 
  3. 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.
    1. E.g. FinTech – “Our schedule is open tonight, and there are great seats available, Section N, Seat A for ABC dollars on Stubhub.  Shall I make the purchase?”
      1. Partner with vendors to drive FinTech business rules.
  4. 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: End to End Framework for Client, Intermediary, and Institutional Services

Is it all about being the most convenient,  payment processing partner, with an affinity to the payment processing brand?  It’s a good place to start; the Amazon Payments partner program.

FinTech noun : an economic industry composed of companies that use technology to make financial systems more efficient

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:

  1. 3K Global Investment Bankers – proprietary CRM platform, including Business Analytics, Business Objects Universe.
  2. 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.
  3. (Multiple Financial Org.) Data Warehouse middleware solutions to assist organizations in managing,  and monitoring usage of their DW.
  4. Global Derivatives firm, migration of mainframe system to C# client / Server platform
  5. Investment Bankers and Equity Capital Markets (ECMG)  build trading platform so teams may collaborate on Deals/Trades.
  6. 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:

  1. 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).
  2. 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.
  3. Transaction Data Usage for Analytics
    • Client initiating transaction,  intermediary parties, and destination of funds may all tell ‘a story’ about the transaction.
    • Every party within a transaction, beginning to end, may benefit from the use of the transaction data using analytics.
      • e.g. Quicken – personal finance management tool; collects, parses, and augments transaction data to provide client  analytics in the form of charts / graphs, and reports.
    • Clear, consistent, and comprehensive data set available at every point in the transaction lifecycle regardless of platform .
      • e.g. funds transferred between financial institutions may  have a descriptions that are not user friendly, or may not be actionable, e.g. cryptic name, and no contact details.
      • Normalizing data may occur at an abstracted layer
    • Abstracted, and aggregated data used for analytics
      • e.g. average car price given specs XYZ;
      • e.g. 2. avg. credit score in a particular zip code.
    • Continued growth opportunities, and challenges
      • e.g. data privacy v. allowable aggregated data
  4. Affinity Brand Opportunities Transaction Management Solution
    • eWallet affinity brand promotions,
      • e.g. based on transaction items’ rules; no shipping
      • e.g.2. “Cash Back” Rewards, and/or Market Points
      • e.g.3. Optional, “Fundraiser” options at time of purchase.
  5. Credit Umbrella: Monitoring Use Case
    • Transparency into newly, activated accounts enables the Transaction Management Solution (TMS) to trigger a rule to email the card holder, if eligible, to add card to eWallet

Is Intuit an acquisition target because of Quicken’s capabilities to provide users consistent reporting of transactions across all sources?  I just found this note in Wiki while writing this post:

Quicken is a personal finance management tool developed by Intuit, Inc. On March 3, 2016, Intuit announced plans to sell Quicken to H.I.G. Capital. Terms of the sale were not disclosed.[1]

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

 Those inhibitors haven’t stopped these firms:

Payments Ecosystem
Payments Ecosystem