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BEC IV > 3. Emerging Technologies > Flashcards

3. Emerging Technologies Flashcards

1
Q

Who provided pmt services historically?

A

Financial institutions.

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2
Q

What are characteristics of pmt system by financial institutions and new vendors?

A

F: Expensive.
N: easy to use, cheaper, seller friendly, reduced costs, increase online sales.

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3
Q

What are examples of vendors/pmt systems?

A

Apply Pay, Samsung Pay, Walmart Pay, Pay Pal, Venmo, Amazon “One-Click” pmts.

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4
Q

What is the goal of emerging pmt processing systems?

A

Simplify pmts for customers, reduce processing fees.

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5
Q

What are strategies for simplifying pmts?

A
  • Reduce customer keystrokes and data entry
  • “Make pmt systems for dummies”
  • Pmt data storage is a customer choice
  • Market the pmt systems
  • Monitor check-out abandonment rates and reasons
  • Train employees in new systems and methods
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6
Q

What is Internet of Things (IoT)?

A

Wide-spread connection of electronic devices to Internet.

Gartner estimates by 2020, >26 billion devices connected to internet.

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7
Q

What are examples of IoT?

A
  • Medicine and agriculture: real-aim data feeds that monitor the status and condition of any living organism (e.g. Fitbit)
  • Insurance: sensory data on road conditions, weather, traffic, driver behaviors, etc.
  • Banking: monitor use and status of ATMs, physical security of offices and buildings
  • Marketing: respond in real-time to customers’ interests and physical proximity
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8
Q

What are goals of IoT?

A
  • Transform business processes and models
  • Enable new products
  • New, advanced analytics: track emerging trends and interest
  • New insights into production processes and customers
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9
Q

IoT: what are accounting implications?

A
  • Automated information collection and data streaming for audits (internal and external) and tax engagements
  • Real-time managerial accounting monitoring data
  • New still sets for CPAs: managing the IoT and the resulting “big data”
  • Biggest change: continuous auditing and monitoring of activities
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10
Q

IoT: risks?

A
  • Privacy, intrusive devices: previously unimagined data available about individuals and their behavior (e.g. medical and financial data)
  • Complexities of data ownership, availability, distribution, storage
  • The creation and management of big data
  • Legal and regulatory implications (e.g. HIPPA)
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11
Q

Automated IT security: authentication: what is the goal for user authentication?

A

Fully integrated, multi-factor security, automated systems.

Only “escalated” authentication is manual.

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12
Q

Automated IT security: authentication: how?

A
  • IoT will increase use of automated security systems

* Authentication in these systems will use multiple identifiers

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13
Q

Automated IT security: authentication: what are the examples of identifiers?

A
  • Biometrics (e.g. finger prints, iris scans, body scans, facial recognition, heart rhythm and rate)
  • Advanced analytics that identify system use patterns (e.g. typical login times, pressure and force on keyboard and mouse)
  • Objects (e.g. cell phones, key cards)
  • Knowledge (challenge questions)
  • Contextual patterns of use that combine the above identifiers using artificial intelligence
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14
Q

Automated IT security: authentication: risks and prognosis?

A

R: Inevitable failures and shake outs as vendors and users experiment
P: in 10-20 yrs, near complete automation of integrated, multi-factor authentication systems

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15
Q

What is HMDs?

A

Head Mounted Display.
Display devices worn on hear (like glasses ) or as part of helmet.
*Include one or 2 small optical displays.
*Fully or partially “immersive”: Provide augmented (partially) or virtually (fully) reality experiences that immerse the user in a digitized experience.
*e.g.: Google glasses, Pokeman

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16
Q

HMDs: risks?

A

*Digital distraction and information overload

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17
Q

HMDs: what does the future look like?

A
  • Convenient and accessible real-tome monitoring

* Less intrusive than “head-down” devices like smart phones

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18
Q

HMDs: implications for accounting? Examples?

A

*Ubiquitous computing
*Real-time data streaming and alerts
*Continuous auditing and monitoring of human and nonhuman activity
Ex: monitoring productive processes, financial results, IT availability

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19
Q

HMDs: what is an important driver?

A

The Interest of Things = combination results in greater flexibility of head movements, lower weight, and easier accessibility to critical visual information and real-time data streaming.

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20
Q

What is gamification?

A

Apply video gaming principles, or badges and points, to engage users in learning - making learning fun based on psychological principles using graphics, design, images, motivation, and narrative (stories) to simulate actual scenarios.

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21
Q

Gamification: applications?

A

To reward employees for engaging in health and wellness activities.
To simulate opportunities for employees to build client relationships and build firm’s brand.
To train for IT security and data privacy compliance.

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22
Q

Big data: what COSO principle is related to this?

A

11 = “The organization selects and develops general control activities over technology to support the achievement of objectives”

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23
Q

Big data: Definition? How did it come about?

A

“Creation, analysis, storage and dissemination of extremely large data sets”

Feasible due to advances in computer storage technologies (e.g. cloud), advanced data analytics, and massive computer power.

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24
Q

Big data: Gartner definition?

A

“High volume, velocity, and/or variety information assets that demand new, innovative forms of processing for enhanced decision making, business insights or process optimization”

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25
Q

Big data: data sources?

A

Ubiquitous computing (i.e. smart phones and wearables, e.g. Fitbit), the Internet of Things, biometrics (i.e. automated human recognition)

26
Q

Big data: what must a firm establish re: governance?

A

Must establish a clear governance structure for big data projects.

  • Responsibilities, scope and limits of project
  • Require a clear purpose, scope and plan
  • Consider qualitative characteristics of info in formatting big data plans
27
Q

Big data: new emerging possibilities?

A

For monitoring of accounting and internal control systems.

28
Q

Big data: what is the source?

A

“Dark data” = sometimes a synonym for “Meta data” (i.e. data about data).
= activity, operational or social media data that is unused or underused.
*Data from business activities that may be reused in analytics, business relationships, or directly monetized (sold).
*Operational and social media data that is collected.

29
Q

Big data: implications?

A
  • will result in expanding existing data warehouses
  • Big data analytics and “smart data”: emerging focus of big data-data mining (discovering data trends), expanded OLAP (online analytical processing).
  • Big data-based audits
30
Q

What is smart data?

A

Another name for big data, which generally refers to both big data and the use of advanced analytic methods on the data.

31
Q

Big data: what are values?

A
  • Uses marketing and sales
  • Monitoring and performance evaluation
  • Operational and financial performance
  • Risk and compliance management
  • Product and service innovation
  • Improving customer experiences and loyalty
  • Data monetization (data sales)
32
Q

Big data: risks?

A
  • Privacy
  • Legal issues and prohibited uses (e.g. medical data and HIPPA)
  • Technology and structure: where and how will be stored and protected?
33
Q

Big data: what are keys in managing initiatives?

A
  • Approach and understanding: top management informed of and engaged in big data initiative, and understand the scope and risks?
  • Data quality: does the data collection and storage process results in accurate, reliable, complete, timely data?
  • Data confidentiality and privacy: does the organization comply with external laws and regulations, and its own internal stds for data confidentiality and privacy?
  • Availability: are disaster recovery and event response plans in place and reliable?
34
Q

Big data: what are relevance to accounting and accountants?

A
  • Possible new roles for CPAs: data scientists of financial information
  • Applications of big data in auditing, tax work, and risk analysis (e.g. big data analysis of risk of material misstatement of an audit client)
  • Big data analysis of our identified risk areas
  • Continuous audit of big data streams
35
Q

Bitcoin: what is it?

A
  • An intangible asset
  • Electronic cash
  • A decentralized currency
  • A network, payment, and accounting system
36
Q

Bitcoin: how is created, traded, and send/received?

A
  • Created by “mining.”
  • Traded and exchanged on a peer-to-peer network (decentralized network).
  • Send or receive bitcoins with an address, like a bank account.
37
Q

Bitcoin: where is it stored?

A

Stored in a “blockchain” i.e. a type of fancy database

38
Q

Bitcoin: what are the reasons to use?

A
  • Independent of any government or central authority (favorite of libertarians)
  • May be anonymous (favorite of criminals, terrorists)
  • Historically: low transaction costs
39
Q

Bitcoin: risks?

A
  • New investments and currencies have higher fraud risk

* Bitcoin investors and users may be targets for fraudsters and criminals

40
Q

Bitcoin: risks of holding and investing in bitcoins?

A
  • No insurance
  • Extreme price fluctuations
  • Possibility of government regulation and restriction on bitcoins
  • Fraud and security issues
41
Q

Bitcoin: what are recovery issues?

A
  • Difficulty of tracing bitcoin transactions
  • International (cross-boarder) use
  • Absence of an issuing authority
  • Difficulty of seizing and freezing bitcoins
42
Q

Bitcoin: what is blockchain?

A
  • A decentralized, distributed ledger
  • An independent, secure, non modifiable audit trial of transactions, stored in open ledger database
  • A part of the invention of bitcoins to provide secure, decentralized accounting
  • A blockchain record exist in a file that consists of “blocks” (i.e. documented transaction records)
43
Q

Bitcoin: what does blockchain require to be useful?

A

Adoption by many users.

44
Q

Bitcoin: what are 3 factors for the security of blockchain?

A
  1. Independent confirmation
  2. Asymmetric encryption
  3. Cheap, fast computing capacity
45
Q

Bitcoin: what are blockchain applications?

A
  1. Smart contracts: enables contract enforcement through mutual block monitoring
  2. Internet of Things: a likely application of “smart contracts”
  3. Open source payment: why use financial institutions and their fees for payments when a cheap, verifiable, open-;edger network is available? Why not make pmts directly between parties?
  4. Financing and crowdfunding: previous examples also illustrates the use of blockchain for financing.
  5. Corporate governance and financial reporting: current, 24/7 availability of financial info.
  6. Supply chain auditing: was my t-shirt made by child labor in Cambodia? Trace its blockchain record.
  7. Predictive analytics: aggregate millions of users expectations about an event. Resulting predictions can improve forecasting of weather, business outcomes, sporting events, elections, etc.
  8. Identify and access management: blockchain ledgers can document characteristics of users that enable unassailable multi factor identification based on transaction record.
  9. Auditing and monitoring: imagine the savings if auditors began with an unalterable, continuously available record of all transactions.
46
Q

Blockchain risks?

A
  1. Hacks, crack, and attacks
  2. Blockchain technology is complex and relies on sophisticated, advanced encryption and networking technologies = users can commit obvious control errors that lead to large losses, opportunity for fraudsters.
47
Q

What is AI?

A

Artificial intelligence.

Creating intelligent hardware and software.

48
Q

What does most AI system use?

A

Harvest and use big data (e.g. machine learning systems are of little value without massive data sets).

49
Q

What are categories of AI?

A
  • Machine learning (analysis)
  • Robotics (activity)
  • Intelligent agents (engagement)
  • Expert systems (analysis and activity)
50
Q

What is machine learning?

A
  • Use big data to learn rules and categories to enable prediction and classification (e.g. neural networks)
  • A common accounting application: classifying journal entries
51
Q

What is robotics?

A
  • Machine-directed observation (e.g. TSA) and activity

e. g. welding, controlling production, manufacturing, and distributing processes

52
Q

What is intelligent agents?

A
  • Computer “agents” that perform tasks (e.g. data harvesting and cleaning)
  • Analysis of market trends (e.g. in purchasing airline tickets)
  • Interact with humans (e.g. Siri)
53
Q

What is expert systems?

A
  • Build and apply expertise in a domain (e.g. configuring Dell system, medical diagnosis)
  • May include machine learning or intelligent agent subsystems
54
Q

What are AI tasks?

A
  • Data harvesting and cleaning. Prepping and cleaning client data (e.g. loan documents)
  • Analyzing numbers (e.g. Netflix suggests what to watch next based on quantitative data about your ratings and viewing habits)
  • Analyzing words, images, and sounds (e.g. analyze vitas for hiring decisions and using Google translate)
  • Performing digital tasks (e.g. automating security systems)
  • Performing physical tasks (e.g. self-driving cars)
  • Self-aware AI
55
Q

AI accounting implications: KPMG?

A

Implemented an AI unit and partnered with IBM Watson initiative.
*IBM Watson: expert system and natural language processing system

56
Q

AI accounting implications: Deloitte?

A

Partnered with many vendors to automate specific audit tasks and processes, including document review, confirmations, inventory counts, disclosures, predictive risk analytics, and client request lists (e.g. Kira Systems).

57
Q

AI accounting implications: PwC?

A

Halo system analyzes accounting journals for risky transactions and to identify inefficient processes.
Automates coding and classification of journal entries.

58
Q

AI accounting implications: EY?

A

Focused on integrating Big Data and advanced analytics into audit practice.
2017: launched AI center (India), hired “Global Innovation Artificial Intelligence Leader”

59
Q

What are AI benefits?

A

Speed, accuracy, and cost

  • ability to scale and speed applications, reduced costs, new applications
  • ability to obtain, clean, and analyze large (all available?) data in real time
  • apply AI to robotics, pattern recognition and complex communication (e.g. natural languages) problems
60
Q

What are AI risks?

A
  • Short-term: AI systems often include the biases of their developers.
  • Medium-term: Lost jobs and disappearing employment, Legal and ethical issues (liability: who is responsible for AI mistakes, privacy, security)?
  • Long-term: The end of humanity? Machine overloads?
61
Q

AI risks: What are 3 biases?

A
  • Data biases: harvesting and creating data sets that omit relevant variables and considerations.
  • Prediction biases: systems that include biased data will generate biased predictions.
  • Learned (“emerging” or “confirmation”) biases: smart machines will learn and integrate the biases of those who train them - machines can be trained to “confirm” the biases of their trainers.
62
Q

AI: what are implications for accounting/auditing work?

A

Evolution of accounting jobs:

  • partnering with AI to monitor and improve results
  • oversight of AI in accounting and auditing tasks
  • when and how tasks should be automated versus not
  • develop new and refining existing AI applications
  • executing human only tasks

BEC IV (12 decks)

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