Risk Log and Comprehensive Risk Report (Risk Type)

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I need your help regarding our Issue. We are comparing is the Risk Type from the Comprehensive Report and the Risk Logs extracted from the Monitor tab. In the Risk Logs there is a column name “Category Type” which we assumed is the same as the Risk type under the Comprehensive Report. Now, if we extract the Comprehensive Report and check the Risk Type and compare it with the Risk Logs that is filtered according to category type the expectation should be the same. We do this to determined the endpoints (there is no endpoint details under Risk type) affected under the Risk type. However, the results is different and the discrepancy is huge.




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Wall Street Veteran Blythe Masters Appointed to Phunware Board of Directors

Former CEO of Digital Asset, CFO of J.P. Morgan’s Investment Bank and Chair Emeritus of Linux Hyperledger Project Appointed as Certified Financial Expert

Phunware, Inc. (NASDAQ: PHUN) (the “Company”), a fully-integrated enterprise cloud platform for mobile that provides products, solutions, data and services for brands worldwide, today announced the appointment of Blythe Masters to its Board of Directors.

Blythe Masters is an experienced financial services and technology executive and currently an Industry Partner at the private equity and venture capital firm Motive Partners. She is the former CEO of Digital Asset – provider of the world’s leading smart contract language DAML – which she led from a startup in 2015 until 2018, serving customers including the Australian Securities Exchange (ASX). She is Chair Emeritus of the Governing Board of the Linux Foundation’s open source Hyperledger Project, International Advisory Board Member of Santander Group, Board Member of OpenBank and Advisory Board Member of the United States Chamber of Digital Commerce, Figure Technologies – the blockchain-powered consumer financial products company – and the residential mortgage exchange, Maxex.

Blythe was previously a senior executive at J.P. Morgan, which she left after 27 years in 2014, following the successful sale of the physical commodities business which she built. Blythe was a member of the Corporate & Investment Bank Operating Committee and the firm’s Executive Committee. Positions at J.P. Morgan included Head of Global Commodities, Head of Corporate & Investment Bank Regulatory Affairs, CFO of the Investment Bank, Head of Global Credit Portfolio and Credit Policy & Strategy, Head of North American Structured Credit Products, Co-Head of Asset Backed Securitization and Head of Global Credit Derivatives Marketing.

Blythe is a past Chair of the Global Financial Markets Association (GFMA), the Securities Industry & Financial Markets Association (SIFMA) and the public consumer finance company Santander Consumer Holdings Inc. (NYSE: SC).

Blythe is currently Co-Chair of the Global Fund for Women, Vice Chair of ID2020, Advisory Board Member and past Board Member of the Breast Cancer Research Foundation, Board Member of the Feminist Institute, and former Chair of the Greater New York City Affiliate of Susan G. Komen for the Cure. Blythe holds a Bachelor of Arts degree in Economics from the University of Cambridge.

“We are incredibly excited and honored to have appointed Blythe to our Board of Directors,” said Alan S. Knitowski, President, Chief Executive Officer and Co-Founder of Phunware. “Her background on Wall Street and her operational credentials and pedigree speak for themselves.”

The Phunware Board of Directors unanimously approved the appointment of Blythe Masters as the Company’s Certified Financial Expert, including her appointment as Chair of the Audit Committee and Member of the Compensation Committee.

“I am looking forward to helping Phunware become a household name on both Wall Street and Main Street,” said Blythe Masters. “The Company sits at the intersection of mobile, cloud, big data and blockchain and I look forward to contributing to its efforts in becoming the global enterprise platform standard for Fortune 1000 digital transformation initiatives.”

Safe Harbor Clause and Forward-Looking Statements

This press release includes forward-looking statements. All statements other than statements of historical facts contained in this press release, including statements regarding our future results of operations and financial position, business strategy and plans, and our objectives for future operations, are forward-looking statements. The words “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “expose,” “intend,” “may,” “might,” “opportunity,” “plan,” “possible,” “potential,” “predict,” “project,” “should,” “will,” “would” and similar expressions that convey uncertainty of future events or outcomes are intended to identify forward-looking statements, but the absence of these words does not mean that a statement is not forward-looking.

The forward-looking statements contained in this press release are based on our current expectations and beliefs concerning future developments and their potential effects on us. Future developments affecting us may not be those that we have anticipated. These forward-looking statements involve a number of risks, uncertainties (some of which are beyond our control) and other assumptions that may cause actual results or performance to be materially different from those expressed or implied by these forward-looking statements. These risks and uncertainties include, but are not limited to, those factors described under the heading “Risk Factors” in our filings with the Securities and Exchange Commission (SEC), including our reports on Forms 10-K, 10-Q, 8-K and other filings that we make with the SEC from time to time. Should one or more of these risks or uncertainties materialize, or should any of our assumptions prove incorrect, actual results may vary in material respects from those projected in these forward-looking statements. We undertake no obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as may be required under applicable securities laws. These risks and others described under “Risk Factors” in our SEC filings may not be exhaustive.

By their nature, forward-looking statements involve risks and uncertainties because they relate to events and depend on circumstances that may or may not occur in the future. We caution you that forward-looking statements are not guarantees of future performance and that our actual results of operations, financial condition and liquidity, and developments in the industry in which we operate may differ materially from those made in or suggested by the forward-looking statements contained in this press release. In addition, even if our results or operations, financial condition and liquidity, and developments in the industry in which we operate are consistent with the forward-looking statements contained in this press release, those results or developments may not be indicative of results or developments in subsequent periods.

About Phunware, Inc.

Everything You Need to Succeed on Mobile — Transforming Digital Human Experience

Phunware, Inc. (NASDAQ: PHUN), is the pioneer of Multiscreen-as-a-Service (MaaS), an award-winning, fully integrated enterprise cloud platform for mobile that provides companies the products, solutions, data and services necessary to engage, manage and monetize their mobile application portfolios and audiences globally at scale. Phunware’s Software Development Kits (SDKs) include location-based services, mobile engagement, content management, messaging, advertising, loyalty (PhunCoin & Phun) and analytics, as well as a mobile application framework of pre-integrated iOS and Android software modules for building in-house or channel-based mobile application and vertical solutions. Phunware helps the world’s most respected brands create category-defining mobile experiences, with more than one billion active devices touching its platform each month. For more information about how Phunware is transforming the way consumers and brands interact with mobile in the virtual and physical worlds, visit https://www.phunware.com, https://www.phuncoin.com, https://www.phuntoken.com, and follow @phunware, @phuncoin and @phuntoken on all social media platforms.

View source version on businesswire.com: https://www.businesswire.com/news/home/20191230005052/en/


PR & Media Inquiries:

Brent Brightwell


T: (512) 537-8301

Investor Relations:

Brendhan Botkin


T: (512) 394-6837


Data Warehouse and Visualizations for Credit Risk Analysis

Most people are dependent on credit to finance vehicles, real estate, student loans, or start small businesses. For financial institutions, assessing credit risk data is critical to determining whether to extend that credit. In this blog, we’ll demonstrate how incorporating data from disparate data sources (in this case, from four data sets) allows you to better understand the primary credit risk factors and optimize financial models.

What’s the best way to make that easy? By using Autonomous Data Warehouse, which gives financial institutions the flexibility to dynamically test and modify analytical models without specialized skills. We’ll demonstrate how Autonomous Data Warehouse makes analyzing credit risk simpler.

Try a Data Warehouse to Improve Your Analytics Capabilities

Analyzing Credit Risk

For many financial institutions, one key performance measure comes to mind more than any other: credit risk. A person’s credit risk score is based on financial health factors including: available credit, debt, payment history, and length of credit history. The financial factors not built into the credit score include income, bank balance, and employment status. But all of these can potentially be used to improve the credit risk model, which ultimately drives more revenue. In this blog, let’s review different data sets that we will use to effectively analyze credit risk.

Understanding the Data Sets

By using data visualizations, data analysts can learn about and effectively segment the market. In this project we are connecting multiple data sources:


Data analysts generate insights by sifting through significant amounts of data that can be used in conjunction with one another. However, data from different departments can often be siloed, making it harder for an analyst to incorporate potentially valuable predictive data into the model. For example, data elements in credit risk analysis include employment history from HR, purchase history from sales, and core financial health reports from finance. By combining these data sources into a single cohesive system, analysts can create more accurate models. Financial institutions can not only reduce costs by strategically identifying their target market segment, but also better monetize their data by continuously tailoring financial products while improving service delivery.

We looked at the following questions:

  1. How are weights assigned to individual financial factors to create a model that predicts the credit risk?
  2. What is the distribution of our target market based on our credit risk model?
  3. What kinds of loans is our target market segment interested in?
  4. How is the rate of homeownership correlated with wealth brackets based on the type of loans our target market is interested in (housing loans)?
  5. What combination of attributes identifies a risk-free customer?
  6. How effective was the targeted marketing campaign based on our segmentation analysis?

To get started, we downloaded the CREDIT_SCORING_100K_V dataset. This is one of the four datasets we will be using in this project. Here’s how the different attributes are displayed in Excel.

Let’s view the data in Oracle Data Visualization Desktop now. There are multiple ways to upload data to Oracle Cloud for analysis using Oracle Autonomous Data Warehouse. For this example, we uploaded the Credit Scoring 100K data set and reviewed the data in Data Visualization Desktop.

Here’s a quick snapshot of the data from Data Visualization Desktop:

1. How are weights assigned to individual financial factors to create a model that predicts the credit risk?

In the pivot table, (on the left) we see different factors that help to determine the potential value of a customer including: credit scores, wealth, education, income, debt, and other financial measures. Each factor is given a weight based on significance and ranked. When we plot this data on a horizontal bar graph visualization, we can see all the financial factors from most to least important. This way we can visually see that a factor like wealth (IV: .54) is 10X more important than family size (IV: .04).

2. What is the distribution of our target market based on our credit risk model?

This shows the probability of good credit for various demographic factors. Adjusting the filters above (when you’re in Data Visualization Desktop) to gain an understanding of what is likely to result in good credit. Each row is a person, so we can see that in our model, most people have a 52.85 or 55.26 percent probability of good credit. From this data, we can perform statistical analysis on the standard deviation to understand the target group of clients with more than 50 percent probability of good credit.

3. What kinds of loans is our target market segment interested in?

In this visualization, we set up a pivot table to target people with a high probability of good credit as our target segment. Then we filter their credit history by delay, duly now, duly past, not taken, and risky.

From this, we can construct a treemap visualization to see the loan type of this target market segment. We see that the most common type of loan includes need based followed by housing, auto, and education loans. More than half of the loans are either need based or housing loans.

4. How is the rate of homeownership correlated with wealth brackets based on the type of loans our target market is interested in (housing loans)?

In this visualization, we use a scatterplot to correlate the credit scores, age, and wealth (on the left). We also use pie charts to understand the rate of home ownership among different income brackets (on the right). In the scatterplot, we see that credit scores are correlated to wealth but not correlated to age. In the pie chart, homeowners are shown in green. Out of those surveyed, 22.5 percent of respondents were homeowners while 35.4 percent were tenants. When broken out by wealth, the rate of homeownership increases as you move up the income bracket.

5. What combination of attributes identifies a risk-free customer?

The network map links uses lines to link variables such as the probability of good credit, family size, and residential status. Each data point is a node and each linkage represents a relationship between two data points. In this visualization, we’ve filtered to only show individuals with more than 50 percent probability of good credit. Drilling down further into the simplified network, we can isolate a node that homeowners with 2-3 children are a demographic that often has a high probability of good credit (see below). We can continue the analysis by looking at individual customer IDs and execute a marketing campaign to acquire low-risk customers. By targeting high-value customers, we optimize a limited marketing budget and increase the effectiveness of our sales promotion.

6. How effective was the targeted marketing campaign based on our segmentation analysis?

In this line graph, we use cumulative lift to measure of how much better prediction results are compared to a baseline. In this model, the red line acts as a baseline and the yellow line represents actual results. As an example, suppose you normally have a 5 percent response rate but your most recent marketing campaign has an astonishing 20 percent response rate. The lift for that model would be 5/20 or 4. Since lift is computed using actual outcomes, analysts can compare how well a campaign performed against data on from previous campaigns.


Oracle Autonomous Database allows users to easily create data marts in the cloud with no specialized DBA skills and generate powerful business insights. It took us fewer than ten minutes to provision a database and upload data for analysis.

Analysts are always looking for ways to create a more accurate credit risk model with data. They ask for analytical capabilities to discover innovative answers to their questions. While analysts are looking for those data insights, leadership wants insights delivered in a clear and concise format to understand the business. IT can’t deal with difficult-to-manage legacy approaches requiring expensive teams with highly specialized skills. And that’s where the Autonomous Data Warehouse comes into play.

Now you can also leverage the autonomous data warehouse through a cloud trial:

Sign up for your free Autonomous Data Warehouse trial today

Please visit the blogs below for a step-by-step guide on how to start your free cloud trial: upload your data into OCI Object Store, create an Object Store Authentication Token, create a Database Credential for user and load data using the Data Import Wizard in SQL Developer:

Feedback and question welcome.

Written by Sai Valluri and Philip Li


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Digital process automation with BPM and blockchain, Part 3: Use blockchain to manage legal documents stored in an enterprise content repository

In this tutorial, we explore a trade finance use case, a Letter of
Credit transaction, that shows you how blockchain can help coordinate the
payment and transfer of goods between buyer and seller banks through the use
of Business Process Management and Enterprise Content Management.


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Making Friends in Dallas

EMC logo

Since 1930, the official State motto of Texas has been “Friendship”.  This is an apt description of the largest GRC user group in the world, RSA Charge, being held in Dallas, Texas, October 17 – 19. In a previous blog, Steve Schlarman shared an overview and highlights of this year’s event.


One of the event tracks this year is “Inspiring Everyone to Own Risk”.  This track brings together risk management practitioners across various industries and geographies to discuss challenges and successes they have experienced managing risk using the RSA Archer Suite of solutions.  This track includes a representative sampling of subjects from each of the Enterprise and Operational Risk Management challenges including: Issues Management, Establishing and maintaining a risk taxonomy and risk register, Self-assessments, Engaging the lines of defense, Third-party risk and performance management, and Business continuity management.


We had a great pool of speaker submissions this year.  In some cases, like Issues management and Third-party risk management, we had so many submissions we turned them into panel discussions so that you can benefit from the collective knowledge of multiple experts in these fields.


Combined with tracks at RSA Charge focused on regulatory and corporate compliance and information security management, practitioners have an opportunity to learn about each of the most important topics facing Operational Risk Managers today, including how to transform technology risk into Business-Driven Security.  In addition, you will have the opportunity to share ideas and learn from your peers, thought leaders, and specialists in these areas as well as see demonstrations of the RSA Archer Suite.  


For those of you that haven’t looked at the complete Agenda, you will find it full of great sessions. We have over 200 submissions from customers and partners, representing over 70 companies from a wide range of industries and geographies, along with a great representation of government agencies.


Yes, hosting RSA Charge in the State of Friendship is very apropos.  You will create and renew friendships with attendees with similar challenges and governance perspectives; learn new and innovative risk management methods; and affirm your best practice approaches.


We are looking forward to seeing you in Dallas!  If you haven’t registered, do so today.


RSA Charge 2017, the premier event on RSA® Business-Driven Security™ solutions, unites an elite community of customers, partners and industry experts dedicated to tackling the most pressing issues across cybersecurity and business risk management. Through a powerful combination of keynote speeches, break-out sessions and hands-on demos, you’ll discover how to implement a Business-Driven Security strategy to help your organization thrive in an increasingly uncertain, high-risk world. Join us October 17 – 19 at the Hilton Anatole in Dallas, Texas.

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