Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.
Watch the presentation here: http://videos.re-work.co/events/36-deep-learning-in-finance-summit-london-2018
Its often not enough to apply the same FOSS concepts and methods to data as you do with code. So, how do you set data free, while not wrecking it, or inadvertently harming people?
Its often not enough to apply the same FOSS concepts and methods to data as you do with code. So, how do you set data free, while not wrecking it, or inadvertently harming people?
Compute your final grade by getting the average of the last 3 columns. Should there be any question or clarification see me on Friday April 13, 2010 from 10 to 12 am at the SLA workroom.
i will still give additional points to those who made the music video. for now this is your actual performance for the 2nd quarter.
This talk is about data science and statistics applied to flight safety in commercial aviation worldwide. In the introductory part, we will stress the importance of monitoring your flight data and show you some real records coming from flight data recorders (aircraft “black boxes”). We will then explain how the data is recorded, downloaded, analysed, converted to safety events and finally, validated by experts in the field - flight data analysts. Data aggregation across many flights will result with statistical images of safety risks in airlines’ operations. However, this valuable tool can turn into a deadly weapon if used negligently – we’ll support this claim with examples. We are convinced the audience will know about some of these traps, regardless of the industry they are coming from, but hopefully there will be something valuable to take home, too. In the second part, we are saying goodbye to the data analyst and the statistician – the two dominant guys from the first part of the presentation. However, a data scientist will stem from valuable experiences and domain knowledges of the two. This guy will walk the audience through three simple, but working examples. The first one is about how we can improve the accuracy of automated analysis by using historic data and a probabilistic, Bayesian approach. The second example is about finding novel safety risks in airlines’ operations by using simple principal component analysis. Lastly, we’ll use a Markov model to detect aircraft which have changed behaviour with respect to frequency of data downloads, so we collect as many flight data as possible. We will try to make this chat as interesting and as interactive as we can and are looking forward to meeting you at this fun and interesting conference!
Wie HDInsight (Hadoop auf Windows Azure), SQL Server 2014 und Excel zusammenspielen
Big Data ist eines der großen Buzzwords der IT-Welt, und doch für viele noch Neuland. In diesem Vortrag diskutieren wir, was Big Data überhaupt bedeutet, und schildern die Rolle der Microsoft Technologien in einem Bereich, der weit mehr als Open Source und Hadoop ist. Hierbei zeigen wir anhand konkreter Szenarien, wie HDInsight, SQL Server 2014 und Excel zusammenspielen, um typische Big Data-Aufgabenstellungen zu lösen!
People Power1.What is the purpose of the event specifications gu.docxkarlhennesey
People Power
1.What is the purpose of the event specifications guide (ESG) and who should receive a copy of it?
Who should be present at the pre-convention meeting and why?
Event Technology
2.A meeting professional needs to inform attendees that a speaker session has been moved from one room to another the morning of the session. Describe three ways event technology can be used to alert attendees.
3.What is the purpose of using technology on site? Please list at least three new onsite technologies tools have been applied to meeting industry.
Legal issues:
4.What are some of the challenges with attrition and cancellation that meeting professionals often confront? Please list three challenges and explain each one.
5.What does event insurance cover? Please list three event insurance coverages and explain each one.
Green Meetings:
6.What are the strategies meeting manager can use to operate a green meeting (at least 3 strategies)? Please explain each one.
7.How can meeting managers involve attendees in green efforts at facilities?
8.Meeting managers can require green efforts in the facilities they utilize. How can facilities encourage meeting managers to hold green meetings?
Closing the meeting:
9.Closing the meeting is usually a very busy time, with everyone anxious to leave. The meeting staff is tired and wants to return home. Please list and briefly introduce actions to take, when close the meeting (at least four actions).
10.What are the differences between “tip” and “gratuity”? What are the criteria help event operator to determine the amount of tip for each individual?
11.What are the guidelines to organize a post-convention meeting?
Getting Started
Getting Started
In this practice you will be working with a variation of the flight data in the workbook entitled Data_Blending_Practice_Starter_File.
Save the workbook to your desktop or a USB drive.
Delays Due to Visibility
Delay Created by Visibility Issues
We’re interested in exploring the relationship between Visibility (Visibility in the ORD Weather data) and Actual Delay at the scheduled departure time (CRS_DEP_TIME in the ORD_Analysis data).
Working in the ORD_Analysis worksheet, add a column after the one you added for WEATHER_ISSUES, name it DEP_VISIBILITY.
Assign the Range Name W_VISIBILITY to the Visibility column data in the ORD_Weather Tab.
Populate the DEP_VISIBILITY column from the range W_VISIBILITY using an array formulaand the same formula elements (IF, MATCH, INDEX) used to bring in the Weather information at CRS_DEP_TIME referencing W_VISIBILITY. Use the IFNA function to resolve the #NA issues that resulted from your formula.
Create a Scatter Plot, in a new worksheet entitled Visibility vs Delay, with VISIBILITY as the y-axis, and ACTUAL_DELAY as the x-axis. It should resemble the chart below. (NOTE: You can't create a scatterplot by inserting a pivot chart. Check Lynda or your favorite internet search engine for how to insert a scatterplot.)
Wha ...
Compute your final grade by getting the average of the last 3 columns. Should there be any question or clarification see me on Friday April 13, 2010 from 10 to 12 am at the SLA workroom.
i will still give additional points to those who made the music video. for now this is your actual performance for the 2nd quarter.
This talk is about data science and statistics applied to flight safety in commercial aviation worldwide. In the introductory part, we will stress the importance of monitoring your flight data and show you some real records coming from flight data recorders (aircraft “black boxes”). We will then explain how the data is recorded, downloaded, analysed, converted to safety events and finally, validated by experts in the field - flight data analysts. Data aggregation across many flights will result with statistical images of safety risks in airlines’ operations. However, this valuable tool can turn into a deadly weapon if used negligently – we’ll support this claim with examples. We are convinced the audience will know about some of these traps, regardless of the industry they are coming from, but hopefully there will be something valuable to take home, too. In the second part, we are saying goodbye to the data analyst and the statistician – the two dominant guys from the first part of the presentation. However, a data scientist will stem from valuable experiences and domain knowledges of the two. This guy will walk the audience through three simple, but working examples. The first one is about how we can improve the accuracy of automated analysis by using historic data and a probabilistic, Bayesian approach. The second example is about finding novel safety risks in airlines’ operations by using simple principal component analysis. Lastly, we’ll use a Markov model to detect aircraft which have changed behaviour with respect to frequency of data downloads, so we collect as many flight data as possible. We will try to make this chat as interesting and as interactive as we can and are looking forward to meeting you at this fun and interesting conference!
Wie HDInsight (Hadoop auf Windows Azure), SQL Server 2014 und Excel zusammenspielen
Big Data ist eines der großen Buzzwords der IT-Welt, und doch für viele noch Neuland. In diesem Vortrag diskutieren wir, was Big Data überhaupt bedeutet, und schildern die Rolle der Microsoft Technologien in einem Bereich, der weit mehr als Open Source und Hadoop ist. Hierbei zeigen wir anhand konkreter Szenarien, wie HDInsight, SQL Server 2014 und Excel zusammenspielen, um typische Big Data-Aufgabenstellungen zu lösen!
People Power1.What is the purpose of the event specifications gu.docxkarlhennesey
People Power
1.What is the purpose of the event specifications guide (ESG) and who should receive a copy of it?
Who should be present at the pre-convention meeting and why?
Event Technology
2.A meeting professional needs to inform attendees that a speaker session has been moved from one room to another the morning of the session. Describe three ways event technology can be used to alert attendees.
3.What is the purpose of using technology on site? Please list at least three new onsite technologies tools have been applied to meeting industry.
Legal issues:
4.What are some of the challenges with attrition and cancellation that meeting professionals often confront? Please list three challenges and explain each one.
5.What does event insurance cover? Please list three event insurance coverages and explain each one.
Green Meetings:
6.What are the strategies meeting manager can use to operate a green meeting (at least 3 strategies)? Please explain each one.
7.How can meeting managers involve attendees in green efforts at facilities?
8.Meeting managers can require green efforts in the facilities they utilize. How can facilities encourage meeting managers to hold green meetings?
Closing the meeting:
9.Closing the meeting is usually a very busy time, with everyone anxious to leave. The meeting staff is tired and wants to return home. Please list and briefly introduce actions to take, when close the meeting (at least four actions).
10.What are the differences between “tip” and “gratuity”? What are the criteria help event operator to determine the amount of tip for each individual?
11.What are the guidelines to organize a post-convention meeting?
Getting Started
Getting Started
In this practice you will be working with a variation of the flight data in the workbook entitled Data_Blending_Practice_Starter_File.
Save the workbook to your desktop or a USB drive.
Delays Due to Visibility
Delay Created by Visibility Issues
We’re interested in exploring the relationship between Visibility (Visibility in the ORD Weather data) and Actual Delay at the scheduled departure time (CRS_DEP_TIME in the ORD_Analysis data).
Working in the ORD_Analysis worksheet, add a column after the one you added for WEATHER_ISSUES, name it DEP_VISIBILITY.
Assign the Range Name W_VISIBILITY to the Visibility column data in the ORD_Weather Tab.
Populate the DEP_VISIBILITY column from the range W_VISIBILITY using an array formulaand the same formula elements (IF, MATCH, INDEX) used to bring in the Weather information at CRS_DEP_TIME referencing W_VISIBILITY. Use the IFNA function to resolve the #NA issues that resulted from your formula.
Create a Scatter Plot, in a new worksheet entitled Visibility vs Delay, with VISIBILITY as the y-axis, and ACTUAL_DELAY as the x-axis. It should resemble the chart below. (NOTE: You can't create a scatterplot by inserting a pivot chart. Check Lynda or your favorite internet search engine for how to insert a scatterplot.)
Wha ...
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...Vighnesh Shashtri
Under the leadership of Abhay Bhutada, Poonawalla Fincorp has achieved record-low Non-Performing Assets (NPA) and witnessed unprecedented growth. Bhutada's strategic vision and effective management have significantly enhanced the company's financial health, showcasing a robust performance in the financial sector. This achievement underscores the company's resilience and ability to thrive in a competitive market, setting a new benchmark for operational excellence in the industry.
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
Horses for Courses: Deep Learning Beyond Niche Applications
1. Michael Natusch, Global Head of AI, Prudential plc
Deep Learning in Finance Summit, 15 March 2018
Horses for courses:
deep learning beyond niche applications
2. 2
Prudential – providing financial security since 1848
2
Asset
Management
Asia & Africa US UK & Europe
Insurance & Savings RetailInstitutional
Distribution &
Asset Allocation
how can we productionalise deep learning here?
3. 3
How can we build an AI product?
intelligent
agent
+
frictionless UX
data UI
+
learning loop
Social
Agents
Customers
Partners
automated
Actions
4. 4 2010 2020
deep
learning
probabilistic graphical models
random
forests XGBoost
Source: Wolfgang Ertel, "Introduction to Artificial Intelligence", Springer Verlag, 2011
algorithms
5. 5
Hospital
what algorithm when??
unit cost
($/transaction)
volume
(# transactions)
Google
correlation is good enough
need causation
probabilistic
graphical
models
gradient-boosted
decision trees
deep
learning
6. 6
some of our recent AI-related releases
6
unit cost
($/transaction)
volume
(# transactions)
service
bot
agent
productivity
robo
advisor+£1m AUM/wk
record
retrieval
75%
automation
audio
search
94% automation
@ 90+% accuracy
claims
mgmt
handwriting
recognition
7. 7
Claims management
+
frictionless UX
new claims
+ historic
claims data
+
learning loop
Social
Agents
Customers
Partners
automated
Actions
2 model
ensembles
mobile UI
(incl. chatbot)
l
10. 10
Neural network architecture
Four convolution layers followed by two fully connected layers form the
architecture of the character recognition model
48x48
Grayscale Image
100
200
300
400
1000
5507
Max Pooling
Max Pooling
Max Pooling
Max
Pooling
Drop
out
Soft-
max
Convolution
Layer 1
Convolution
Layer 2
Convolution
Layer 3
Convolution
Layer 4 Fully
Connected
Fully
Connected
11. 11
Field Image Output Correct Characters
Accuracy (1=Correct,
0=False)
Total / Correct Correct Percentage
Name of
Policyowner
1 1 1 3/3 100.00%
Name of Life
Assured
0 0 1 3/1 33.33%
Name of
Employer
ABC 0 1 1 1 1 1 1 1 1 9/8 88.89%
Residential
Address A
1 1 1 1 1 1 1 1 1 1 1 1 1 1 14/14 100.00%
Signs and
symptoms
1 1 2/2 100.00%
Name of
Physician /
Hospital
1 0 0 1 4/2 50.00%
Name of
Physician
1 1 2/2 100.00%
What is / are the
underlying
cause(s) for final
diagnosis?
1 1 1 0 4/3 75.00%
41/34 82.93%
Chen Dawen
Chen Dawen
ABC Logistics Limited
Hong Kong Happy Garden,
11th floor, Room A
stomach ache
Mary Hospital
Li Wen
food irritation
Performance on Pru Hong Kong test form
12. 12
Chinese English Digits
400GB of labelled handwriting data
covering the 5,507 most frequent
characters
Microsoft/NIST MNIST
Python - Tensorflow Python – Tensorflow/Microsoft API Python - Tensorflow
CNN CNN + Microsoft API CNN
Prudential Hong Kong Hospital
Claim Form
Prudential Financial Planning Form Prudential Financial Planning Form
85% 89% 98%
Training Data
Framework
Model
Architecture
Test Data
Accuracy
Model summary
14. 14
Prototypes
a Alpha prototypes are typically created in a hothouse
to quickly prove user experience and functionality fit
a+ Alpha+ prototypes are matured to mimic real systems
and responses, and are ready to be piloted with
colleagues for initial feedback
b Beta prototypes are one step away from production,
having been rapidly iterated upon with feedback from
customers and colleagues
W Omega projects are live, operational, robust and
defect free
15. 15
Prototyping accuracy
Case Load Min AUC equivalent
a ~ 102 ≥ 50%
a+ ~ 103 68.3% 1 s
b ~ 104 88.8%
Wmin
~ 105 95.4% 2 s
Wmax
~ 108 99.99994% 5 s
16. 16
Potential health insurance use cases for this
• Claims & underwriting
– Automation of existing from processing
– Leading to end-to-end process automation
• Fraud, waste and abuse
– Identify claims patterns across customers, agents, providers and clinicians, leading to
identification of fraud and collusion, wasteful practices and abuses
– Can be extended to a ‘real-time’ system, feeding into claims & underwriting
– Enables a learning loop for the automated process
• Customer insight
– Faster feedback on emerging trends across customers and conditions
17. 17
How can we build an AI product?
intelligent
agent
+
frictionless UX
data UI
+
learning loop
Social
Agents
Customers
Partners
automated
Actions
18. 18
Best practice for implementing AI successfully
• data
• tooling
• infrastructure
• people
• APIs
“technical”
• start small and build stuff
• experimentation is cheap
• collaboration, co-location, cross-
functional, time-boxed
• iterate frequently
• drive actions & learn continuously
from their outcomes
“cultural”
build iteratively improving prototypes and measure their accuracy
19. 19
5 maturity levels for building AI products
1. historic data analytics (including advanced analytics techniques such
as clustering, social media analysis, basic NLP etc.)
2. stand-alone machine learning models that are integrated into the
systems stack and drive actions on real-time data streams
3. predictive models that are exposed via APIs and conversational
interfaces
4. automated, continuous learning loops based on batch model re-runs
and real-time data streams on a personalized user basis
5. learning loops that automatically and continually redefine and
implement product, service and channel offerings – autonomously