ARM 401-A4: Data-Driven Decision
Making
2 Data-driven decision making approaches: - correct answer-Descriptive and Predictive
Both of these examples are predictive models that may be useful as repeatable, analytical,
data-driven techniques for risk management decision making.
2 Drawbacks of Neural Networks - correct answer-1) can be overtrained
2) that the processes undertaken by the network during the hidden phase may be too
incomprehensible to be evaluated thoroughly enough.
3 forms of modern data analysis - correct answer-1) Text mining
2) Neural networks
3) Social network analysis
4 Steps in Text Mining Process - correct answer-1) Retrieve and prepare the text
2) Unstructured data needs to be converted into structured data before a modeling algorithm
can analyze it. Structured data stores info in a precise, consistent format that can be
searched more easily
3) Different techniques are used to create a data mining model to help the organization
achieve its goals. These techniques involve identifying previously undetected groups of data,
finding the most similar instances of data, and predefining certain attributes as target
variables.
4) Evaluate the models effectiveness in multiple areas. Obviously, no single approach to
collecting and examining data will give an organization all the answers it seeks, and a text
mining model's predictions are not always correct.
5 Characteristics That Make Data Big - correct answer-1) Volume
2) Variety
3) Velocity
4) Veracity
5) Value
5 Step Process for Data-Driven Decision Making - correct answer-1. Defining the risk
management problem
2. Gathering quality data
3. Analyzing and modeling the problem (descriptive or predictive)
4. Determining insights: by identifying trends, relationships, behavior, and events
5. Making decisions
A potential use of Neural Networks in risk management - correct answer-To compare the
risks of proposed & current projects w/ historical results. Data regarding both successful and
failed past projects is entered into the network. Then it takes data relating to the proposed or
current project so that the network can perform its predictions about the probability of
, success. Info about the current project can also be analyzed to predict the probability of
success at each stage of the project.
A risk manager for a grocery store wants to see if there is a correlation between the
frequency of customer slip and fall accidents, and the time of day. Which one of the following
is a relatively simple data analysis technique that the risk manager could use to determine
this correlation? - correct answer-Bubble plot
Accuracy - correct answer-In model performance evaluation, a model's correct predictions
divided by its total predictions.
This is simply a measure of how often the model predicts the correct outcome. It is found by
dividing the number of correct predictions by the total number of predictions (right or wrong).
Big data - correct answer-Sets of data that are too large to be gathered and analyzed by
traditional methods.
Big Data includes both _____ and _____ data - correct answer-Structured and unstructured
data
Centrality measure - correct answer-In a social network context, the quantification of a
node's relationship to other nodes in the same network.
Helps determine efficiency of the flow between social network connections
-Degree
-Closeness
-Betweenness
Centrality measure: Betweenness - correct answer-The measure of how many times a
particular node is part of the shortest path between two other nodes in a network.
Centrality measure: Closeness - correct answer-The measure of the average distance, or
path length, between a particular node and the other nodes in a network
Centrality measure: Degree - correct answer-A measure of the connections each node has
Classification tree - correct answer-A supervised learning technique that uses a structure
similar to a tree to segment data according to known attributes to determine the value of a
categorical target variable.
Cluster analysis - correct answer-A model that determines previously unknown groupings of
data. Cluster analysis can be used to search data for previously unknown information or
relationships.
Cluster analysis is not so much a typical statistical test as a collection of algorithms that put
data into groups or clusters according to well-defined similarity rules. It's commonly used
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