Cost of Auditing the US Department of Defense
November 18, 2020
And you thought that your accountant charged too much for doing your taxes. “Pentagon Fails Audit Yet Again, Could Pass around 2027, Comptroller Says” pegs the fees for the Pentagon audit in 2019-2020 at $203 million this year.
How did the audit team conduct its work?
Around 1,400 auditors tested the systems and record-keeping processes on weapons systems, military personnel and property around the world in 100 site visits, 530 virtual visits and samples. The process resulted in 24 standalone audits, comprising the overall audit.
Interesting. Not H&R Block I presume.
Stephen E Arnold, November 18, 2020
Financial Crime: Who Is Winning? Banks, Bad Actors, or Enforcement Authorities
November 17, 2020
I read “Only 1% of Laundered Cash in EU Is Detected — ABN AMRO Wants to Improve That.” The article reports:
Detecting money laundering is like mixing the perfect cocktail.
And how many outstanding mixologists in addition to a high profile outfit like a certain bank in Hong Kong or a top dog in Manhattan are there in the financial crime enforcement units around the world? If the data in the write up are accurate, not too many.
The article points out:
Globally, estimates suggest between $800 billion and $2 trillion in laundered money flows through the financial system every year, and an overwhelming majority of it goes undetected. The Netherlands alone sees $16 billion in criminal money flowing through its financial system.
Then this item of information is provided:
…the European Commission found just 1% of an estimated $190 billion in laundered funds were successfully confiscated between 2010 and 2014.
What are the principal conduits for money transfers? Let’s see. Maybe banks? What is ABN AMRO? According to Wikipedia, the entity is — wait for it — a bank. Why does ABN AMRO, the number three bank in the Netherlands want to do better in this sphere of activity? Just a “good” bank I assume.
Stephen E Arnold, November 17, 2020
YouTube Factoid
November 10, 2020
Investor Place published “YouTube Is Now Google’s Biggest Growth Engine and Could Be Worth $200 Billion on Its Own.” The write up contains an interesting factoid which may be semi-true.
YouTube is now nearly 20% of Google’s business, and it’s growing three times faster than the rest of the company.
After decades of effort, Google has another revenue stream, based on advertising and subscriptions.
So what?
There’s the $200 billion and the “could”.
Stephen E Arnold, November 10, 2020
Researchers Find Narrow Path to Increased Profits with AI Tech
November 6, 2020
Uh oh—AI and machine learning may not deliver value to most enterprises that adopt the technology. Researchers at MIT Sloan Management Review have found more than half the companies they studied are deploying AI solutions, but fewer than 10% of those are reaping significant financial benefits. If yours is one of those companies, or is considering AI tools, you will want to check out the researchers’ report, “Expanding AI’s Impact with Organizational Learning.” It turns out even with organizations that correctly address the basics, “like having the right data, technology, and talent, organized around a corporate strategy,” only 20% are seeing increased financial gains.
What is the difference? The team found companies that approach learning as a two-way street do the best with AI resources. And that is not a simple task. The report explains:
“Our research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. Their strategic focus is organizational learning, not just machine learning. Organizational learning with AI is demanding. It requires humans and machines to not only work together but also learn from each other — over time, in the right way, and in the appropriate contexts. This cycle of mutual learning makes humans and machines smarter, more relevant, and more effective. Mutual learning between human and machine is essential to success with AI. But it’s difficult to achieve at scale. Our research — based on a global survey of more than 3,000 managers, as well as interviews with executives and scholars — confirms that a majority of companies are developing AI capabilities but have yet to gain significant financial benefits from their efforts. … Our key finding: Only when organizations add the ability to learn with AI do significant benefits become likely. With organizational learning, the odds of an organization reporting significant financial benefits increase to 73%.”
Interested readers should navigate to the report, kindly supplied in full for free. Section 1 is the executive summary and section 2 describes an example firm that met with success. One might skip section 3, which covers the disappointed, and focus on sections 4-8. That is where the researchers lay out, in great detail, the approach that could mean the difference between a wasted investment and a nearly 75% chance of significantly increased income. We suggest those who wish to be in the latter category study it carefully.
Cynthia Murrell, November 6, 2020
Technical Debt with Cats: Lots of Cats
October 29, 2020
Cats are fine. Lots of cats can trigger a different reaction. I liked “Technical Debt: Why It’ll Ruin Your Software.” I ignored the cats and focused on the information payload of the article. The author does a good job of explaining what a number of people [a] ignore, [b] do not understand, and [c] miss the connection with cost and time over-runs.,
I circled three items in the write up:
First, I circled this passage:
The moment John chose the faster and easiest solution for him was the moment that the Technical Debt was inserted in the code.
The idea is that in order to “get ‘er done,” the Corona virus of cost, complexity, and chaos was let loose. The “faster and easiest” method is everywhere. Like a person with an addiction the individual will not admit, there is no single step toward remediation. The remediators will use the same method.
Second, I noted this diagram:
The chart makes clear what people under pressure often ignore. Costs are rising, and they may not be controllable. How much change has the core of Google search undergone in the last 20 years. Who wants to dig into the guts and deal with some of the interesting problems which exist? Answer: No one who wants a promotion and a chance to start a VC firm.
Third, the future is smart software:
In a realistic and respectable world, machines should take care of these situations, and not us.
Yep, and software will be just wonderful.
Stephen E Arnold, October 29, 2020
Matroid: Not Just Math, a Reminder That Google Is Not Search
October 15, 2020
For many people Google is search. Need a pizza? Google it. But for rick media in contexts like streaming video, Google has pizza cheese on its chin.
A venture funding information service called Finsmes published “Matroid Raises $20 Million in Series B Funding.” Add to the firm’s earlier funding, the company has tallied about $33 million to fuel its innovation engine.
Founded in 2016, the company works at the intersection of machine learning (smart software) and image analysis (more smart software). The Finsmes article states:
The company plans to use the new funding to accelerate product development and go-to-market expansion in manufacturing, industrial IOT (IIOT), and video security markets. Led by Reza Zadeh, CEO and Founder, Matroid Matroid is a studio for creating and deploying detectors (computer vision models) to search visual media for people, behavior, objects, and events — no programming required. Once a detector is developed, Matroid can search any live stream or recorded video, providing real time notifications when the object of interest has been detected. Customers use it in construction, manufacturing, security, media, retail and other industries.
Real time analysis of streaming video is a very important search problem. Despite the perception that “Google is search,” the market for a solution is hefty.
Observations:
- The name of the company is borrowed from math wonks
- Law enforcement and intelligence agencies need a solution that works to deal with the video data available to investigators
- Google’s YouTube search illustrates that ad-supported, good enough methods which rely on a creator to index products or tag videos are examples of old-school thinking, maybe Internet dinosaur thinking.
The company will require additional funding. Nailing real time streaming video knowledge generation requires a large hammer.
Stephen E Arnold, October 15, 2020
Chinese Investment: Some Good Points, Some Important Points Left Out
October 15, 2020
China is probably not on the minds of most US LinkedIn users. Based on my scanning of the “Home” page, there are TikTok style videos for consultants, job seekers, and start ups flogging a range of Gen X, Gen Y, and Millennial services.
Nevertheless, I want to highlight “The Shadow War Between Alibaba and Tencent: Conducting Warfare Through Startups.”
The essay begins with this assertion:
In the West, corporate VCs are often labeled with ‘dumb money’ or being the M&A department in disguise. In China, at least for Alibaba and Tencent, they are kingmakers that can outcompete most financial VCs. They have longer time horizons and deeper pockets than institutional VCs, and bring actual value-adds like consumer traffic and supporting product ecosystems such as payment infrastructure. The battle between every startup and incumbent comes down to whether the startup gets distribution before the incumbent gets innovation. In the West, startups can buy consumer distribution as social networks all monetize through advertising. In China, since Alibaba nor Tencent rely on advertising revenue, distribution can’t be brought as easily, it has to be earned. It’s not a surprise that among the waves of startup contenders, Alibaba and Tencent’s picks (and for a time, Baidu) are always among the favorites to win.
We noted two important points tucked deep in the essay.’
The first makes this observation:
For starters, the user-centric approach of Chinese tech means that every battle for consumer’s attention is a relevant one. For both, strategic investments allow increased usage of their respective product ecosystems.
The second offers:
The shadow war has become global, as both Alibaba and Tencent have their sights set increasingly on South East Asia and India. We are probably going to see a similar pattern play out in these regions too…
Several points seemed irrelevant to the author; for example:
- The linkages between certain successful Chinese companies and the Chinese government
- The “competition” between and among high profile, government-allied Chinese firms is a less-than-subtle way to explain the “competition” that exists in the quasi-market driven country. Who is fooled?
- The data generated by these systems can be available to the Chinese government. Data can act like virtual puppet strings, just more difficult to see and disengage.
Nevertheless, the write up reminds me about a belief about the US investment set up: “Dumb money.” Gentle, but evidence about the author’s attitude.
Stephen E Arnold, October 15, 2020
Be an Entrepreneur: The Venture Outfits Need You
October 2, 2020
Institutional Investor ran an interesting story. No, that is not an oxymoron. Really. “The Pervasive, Head-Scratching, Risk-Exploding Problem With Venture Capital.”
I noted this passage:
Two-thirds of venture deals fail, researchers have found. With such a high mortality rate, a VC fund’s actual ending portfolio size is merely one-third of its invested companies’. So to arrive at an exposure with 20 to 70 companies, a fund needs a starting portfolio of 60 to 210 startup investments. Very few funds meet this size.
Without wrestling with the assumptions or the math, I thought this statement was fascinating:
Significant portions of the average come from very few outlier deals.
Now the assertion:
The golden rule for investors into the venture asset class must therefore be: Build a portfolio of 500 startups, with 100 companies being the absolute minimum.
Okay, how many venture firms do you know that have a portfolio of 500 start ups?
Then the question, “Why not buy a fund of VC funds?” Answer:
Based on industry studies, funds of funds frequently lack diversification across gender and race.
Nervous? Not to worry. Here’s why:
Is venture capital a risky asset class? No. Most VC funds choose to act in a risky manner by not diversifying, but that does not make the asset class risky. To de-risk venture capital, CIOs simply need to acknowledge that VC math is different from public markets math. The importance of low-probability, excess-return-generating investments means that proper diversification requires a portfolio of at least 500 startups.
But most VC firms don’t have 500 or more start ups in their portfolio? That’s what the write up said.
Does this seem to be reassuring?
Stephen E Arnold, October 2, 2020
Palantir Technologies: Minor Questions Remain
October 1, 2020
DarkCyber noted “Techie Software Soldier Spy: Palantir, Big Data’s Scariest, Most Secretive Unicorn, Is Going Public. But Is Its Crystal Ball Just Smoke and Mirrors?” The write up joins the caravan of publications digging into the ins and outs of the intelware business.
There are precedents for a vendor of specialized services becoming a public company. One example is Verint, and there are others. Sometimes the lineage of an intelware company can be difficult to figure out. There are start ups in Cypress; there are partnerships in Herzliya; and there are Byzantine limited liability operations in midtown Manhattan.
What’s striking about Palantir is that the coverage has been content with the jazzy bits. DarkCyber understands the need to create buzz and capture eyeballs. The write up uses an interesting quotation from Admiral Poindexter, an interesting person who may be qualified to explain intelware:
“When I talked to Peter Thiel early on, I was impressed with the design and the ideas they had for the user interface,” Poindexter told me recently. “But I could see they didn’t have — well, as you call it, the back end, to automatically sort through the data and eliminate that tedious task for the users. And my feedback from the people who used it at the time, they were not happy with it at all. It was just much too manual.”
DarkCyber wondered:
- Why the write up did not explore the i2 Analyst’s Notebook vs.. Palantir legal matter. That activity suggested that Palantir may have had some interest in a proprietary file format and allegedly worked in interesting ways to obtain closely guarded information. A related question is, “Why would bright start up engineers resort to allegedly questionable methods to figure out a file format?” Too bad the write up ignores a legal matter which illuminates Palantir’s methods.
- Why is Palantir running into the revenue ceiling which other vendors of search and content processing systems for government entities hit? Are there too few customers? Did Autonomy, another search and content processing company, bumped into the revenue ceiling too? Is there a elephant standing in a pool of red ink in the accounting departments of some search and content processing companies?
- Why are intelware vendors offering their products and services under generous free trials programs to the known customers with allocated funds for such systems? And in parallel, the vendors are working overtime to find someone with deep pockets to buy these start ups?
- How similar are the products and services of intelware vendors? Why is innovation confined to graphics and innovation confined to recycling ideas in circulation for decades? One of the DarkCyber team observed, “Isn’t Palantir Gotham Titan the old Analyst’s Notebook with a pop up wheel on the right mouse button?” (I hire skeptical and maybe slightly cynical engineers I think.)
- Could it be that in the “real world” of fast-moving events the intelware vendors’ products don’t work all that well? Is it time for deeper analysis of comparable products and services? How does Palantir stack up against Voyager Labs’ offerings or the the LookingGlass system.
- Why doesn’t smart software do a better job of importing data? What has Datawalk figured out that eludes the Palantirians?
- Why do some Palantir Gotham installations remain idle? Is it because even the simpler interface is too quirky to use when real-time events generate pressure? Is it difficult for some licensees to allocate staff to use the system in order to become masters of the dataverse?
- Why haven’t Wall Street pushes generated more revenue? What happened to the Thomson Reuters’ deal?
- How long did it take Palantir to stand up its first version of its system after the core team decided the move forward with Gotham? (If you know the answer, write benkent2020 @ yahoo dot com. We know the answer and the winner will receive a copy of CyberOSINT: Next Generation Information Access. Free too. Almost like a trial of the products and services from an intelware start up.)
There are other questions the DarkCyber team considers important as well. Perhaps a “real news” outfit will dig into the intelware market, track the technologies, the inter-company tie ups, and the use cases or in some cases the dis-use cases for these products and services?
DarkCyber, however, finds the idea of Palantir’s going public interesting. Was the point of the exercise financial escape for increasingly concerned investors and grousing employees? Too many questions and too few answers still I think.
Stephen E Arnold, October 1, 2020
FinTech to TechFin: What Is With the Word Position Swap
September 29, 2020
I read an interesting essay called “A Look At The Power Shift From FinTech To TechFin.” I think the main idea is that banks are in the danger zone just as newspapers were and Main Street retail stores are.
I circled this passage:
With millennials becoming more and more comfortable using smartphones to pay for Uber, AirBnB, Amazon etc, the need for customer disintermediation arose and was well addressed by the FinTechs who had no overheads of physical distribution and were sitting on a lot of data. The ability to use non-traditional financial data, utilize SMS, utility bill payments and shopping history to build more accurate credit scoring helped the FinTech prove to be a credible challenge to the traditional lenders.
Okay. Thumbtypers on the march.
We also noted:
There is a need for an urgency to reposition and reinvent the traditional FinTech models as both, the customer expectation, as well as the landscape, is changing at lightning speed. There definitely would be tremendous action in the entire financial services space, including FinTech startups, within the next 2 years.
Several observations:
- Big shoulder financial outfits may such oxygen out of the space; for example, Goldman and Apple
- Regulators could — although it seems unlikely — might curtail monopolistic behavior in both banks and the rarified world of the FAANGs
- Consumers like those who pay a penalty for being 99 percenters might propel social pushback with more momentum.
Yes, there is a need for adaptation. I am not sure a marketing pitch is going to get the result the author views as necessary.
Stephen E Arnold, September 29, 2020
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