How Big Data helps banks know their customers betterHEXANIKA
Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
How Big Data helps banks know their customers betterHEXANIKA
Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
CGT Research May 2013: Analytics & InsightsCognizant
A new survey conducted by Consumer Goods Technology (CGT) and sponsored by Cognizant explores how consumer goods companies are approaching data management strategies and usage.
Achieving Better Credit and Collections with FinancialForce Accounting & Chatter3Sixty Insights
Between sales, credit, collections, and customers, there can be a lot of people—and a lot of information—involved in Accounts Receivable. This infographic provides a quick summary of how Chatter connects the different parties, what characteristics information must have to be useful, and what benefits companies have seen through their use of this enterprise social media tool in their AR operations.
No two companies are alike – and their social media strategy shouldn’t be either. How do company values, key customers, and business goals transfer into implementation and success? How do you test, evaluate, and decide which programmes should continue and which should not?
This workshop will explore a set of data driven methodologies that use data already available in an organisation, and online, and how to apply metrics and experimentation to drive successful social media strategy.
The more you know about your own data, the more likely you are to develop programmes that your customers and employees will actually use.
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
Commercial banking outlook: Views from bankers, disruptors and innovatorsMichael Horrocks
Commercial banking outlook: Views from bankers, disruptors and innovators. A five forces analysis on the banking industry and the top challenges facing commercial banking executives.
10 Enterprise Analytics Trends to Watch in 2019 MicroStrategy
View insights from Forrester analyst Mike Gualtieri, Constellation Research’s Ray Wang and Doug Henschen, Ventana Research’s Mark Smith and David Menninger, IDC’s Chandana Gopal, Marcus Borba, Ronald van Loon and other top analytics and business intelligence thought leaders.
10 Enterprise Analytics Trends to Watch in 2020MicroStrategy
As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights on the top enterprise analytics trends to watch from leading BI, analytics and digital transformation influencers including analysts from Forrester, IDC, Constellation Research, Ventana Research and more.
From artificial intelligence and mobile intelligence, to the explosion of data and data sources, to some very human factors, we hope you’ll find this gathering of insights (plus the patterns and themes that have emerged here) a valuable resource for taking action now, but also looking and planning ahead to become an Intelligent Enterprise.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
On behalf of SBI Consulting I’ve made a webinar on September 25th about Data Monetization.
In the post covid-19 era, transformation of businesses to govern their data more as an asset will become of huge importance. Becoming more data driven and digital will only increase at an unseen pace.
The essence of this transformation and the emphasis will be on Data Monetization. Monetizing your data assets will be of vital importance if you’d want to remain competitive and survive & thrive in the new normal.
In this webinar “Data Monetization in a post-Covid era”, I cover topics such as:
What does Data Monetization entails
Why Data Monetization is important for your business
How does the post-Covid era impacts this monetization process
What do we mean with Infonomics and Data Debt
The 5 key takeaways to get started with Data Monetization
The outcome? A good understanding of Data Monetization and practical insights to get going immediately!
Trends 2013: Five Trends Shaping The Next Generation Of North American Digita...Mitek
Released: May 2013
In this report, Forrester explores five trends that will affect the next generation of digital banking. Rising digital customer expectations, advances in technology, and continued digital disruption threats from outside the industry will change the game for banks and credit unions.
Peer-to-peer lending companies provide online platforms that can quickly pair borrowers seeking a loan with investors willing to fund the loan at an attractive rate. Since these loans are unsecured and companies creating the market generally do not invest their own capital, neither borrowers nor companies assume any risk. Entire credit risk is born by investors. Literature shows that credit risk depends upon borrower characteristics, loan terms and regional macroeconomic factors. To help investors identify unsecured loans likely to be fully paid, a machine learning algorithm was developed to forecast probability of full payment and probability of default.
Training and input data consisted of historic loans’ data from Lending Club and state level macroeconomic data from government and organizational sources. A logistic regression was
shown to provide optimal results, effectively sequestering high risk loans.
Team Members:
Archange Giscard Destine
ad1373@georgetown.edu
linkedin.com/in/agdestine
Steven L. Lerner
sll93@georgetown.edu
linkedin.com/in/sllerner
Erblin Mehmetaj
em1109@georgetown.edu
www.linkedin.com/in/erblinmehmetaj
Hetal Shah
hrs41@georgetown.edu
linkedin.com/in/hetalshah
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
CGT Research May 2013: Analytics & InsightsCognizant
A new survey conducted by Consumer Goods Technology (CGT) and sponsored by Cognizant explores how consumer goods companies are approaching data management strategies and usage.
Achieving Better Credit and Collections with FinancialForce Accounting & Chatter3Sixty Insights
Between sales, credit, collections, and customers, there can be a lot of people—and a lot of information—involved in Accounts Receivable. This infographic provides a quick summary of how Chatter connects the different parties, what characteristics information must have to be useful, and what benefits companies have seen through their use of this enterprise social media tool in their AR operations.
No two companies are alike – and their social media strategy shouldn’t be either. How do company values, key customers, and business goals transfer into implementation and success? How do you test, evaluate, and decide which programmes should continue and which should not?
This workshop will explore a set of data driven methodologies that use data already available in an organisation, and online, and how to apply metrics and experimentation to drive successful social media strategy.
The more you know about your own data, the more likely you are to develop programmes that your customers and employees will actually use.
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
Commercial banking outlook: Views from bankers, disruptors and innovatorsMichael Horrocks
Commercial banking outlook: Views from bankers, disruptors and innovators. A five forces analysis on the banking industry and the top challenges facing commercial banking executives.
10 Enterprise Analytics Trends to Watch in 2019 MicroStrategy
View insights from Forrester analyst Mike Gualtieri, Constellation Research’s Ray Wang and Doug Henschen, Ventana Research’s Mark Smith and David Menninger, IDC’s Chandana Gopal, Marcus Borba, Ronald van Loon and other top analytics and business intelligence thought leaders.
10 Enterprise Analytics Trends to Watch in 2020MicroStrategy
As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights on the top enterprise analytics trends to watch from leading BI, analytics and digital transformation influencers including analysts from Forrester, IDC, Constellation Research, Ventana Research and more.
From artificial intelligence and mobile intelligence, to the explosion of data and data sources, to some very human factors, we hope you’ll find this gathering of insights (plus the patterns and themes that have emerged here) a valuable resource for taking action now, but also looking and planning ahead to become an Intelligent Enterprise.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
On behalf of SBI Consulting I’ve made a webinar on September 25th about Data Monetization.
In the post covid-19 era, transformation of businesses to govern their data more as an asset will become of huge importance. Becoming more data driven and digital will only increase at an unseen pace.
The essence of this transformation and the emphasis will be on Data Monetization. Monetizing your data assets will be of vital importance if you’d want to remain competitive and survive & thrive in the new normal.
In this webinar “Data Monetization in a post-Covid era”, I cover topics such as:
What does Data Monetization entails
Why Data Monetization is important for your business
How does the post-Covid era impacts this monetization process
What do we mean with Infonomics and Data Debt
The 5 key takeaways to get started with Data Monetization
The outcome? A good understanding of Data Monetization and practical insights to get going immediately!
Trends 2013: Five Trends Shaping The Next Generation Of North American Digita...Mitek
Released: May 2013
In this report, Forrester explores five trends that will affect the next generation of digital banking. Rising digital customer expectations, advances in technology, and continued digital disruption threats from outside the industry will change the game for banks and credit unions.
Peer-to-peer lending companies provide online platforms that can quickly pair borrowers seeking a loan with investors willing to fund the loan at an attractive rate. Since these loans are unsecured and companies creating the market generally do not invest their own capital, neither borrowers nor companies assume any risk. Entire credit risk is born by investors. Literature shows that credit risk depends upon borrower characteristics, loan terms and regional macroeconomic factors. To help investors identify unsecured loans likely to be fully paid, a machine learning algorithm was developed to forecast probability of full payment and probability of default.
Training and input data consisted of historic loans’ data from Lending Club and state level macroeconomic data from government and organizational sources. A logistic regression was
shown to provide optimal results, effectively sequestering high risk loans.
Team Members:
Archange Giscard Destine
ad1373@georgetown.edu
linkedin.com/in/agdestine
Steven L. Lerner
sll93@georgetown.edu
linkedin.com/in/sllerner
Erblin Mehmetaj
em1109@georgetown.edu
www.linkedin.com/in/erblinmehmetaj
Hetal Shah
hrs41@georgetown.edu
linkedin.com/in/hetalshah
EXAMINING IMPACTS OF BIG DATA ANALYTICS ON CONSUMER FINANCE: A CASE OF CHINAIJMIT JOURNAL
The use of Big Data analytics for business improvements is a vital strategy for survival. In this paper, we
report a study that investigates the role of BD analytics on consumer finance, credit card finance in
China—a research area that has largely remained unexplored. The largeness and diversity of Chinese
consumer market merit an urgent attention and understanding of role of BD analytics is significant both
theoretically and managerially. This study achieves that target. Given the exploratory nature of study, we
take a qualitative approach. We conduct approximately 30 interviews with baking and finance sector
respondents. The data will be recorded, transcribed and translated. We will analyze data using content
analysis / thematic analysis technique.
Examining impacts of big data analytics on consumer finance a case of chinaIJMIT JOURNAL
The use of Big Data analytics for business improvements is a vital strategy for survival. In this paper, we report a study that investigates the role of BD analytics on consumer finance, credit card finance in China—a research area that has largely remained unexplored. The largeness and diversity of Chinese
consumer market merit an urgent attention and understanding of role of BD analytics is significant both theoretically and managerially. This study achieves that target. Given the exploratory nature of study, we take a qualitative approach. We conduct approximately 30 interviews with baking and finance sector respondents. The data will be recorded, transcribed and translated. We will analyze data using content analysis / thematic analysis technique.
Eyes wide shut: Global insights and actions for banks in the digital ageIgnasi Martín Morales
We know what banks want to achieve.
We know how they can achieve it. What we
want to explore further is how close banks
are to achieving their digital goals, both
now and over the next few years. So we
asked 157 senior IT executives, CIOs, CTOs
and other heads of technology spanning
14 primary markets for their thoughts on
digital banking’s potential for today – and
tomorrow. This paper presents the findings
of our study and examines the implications
of our findings for banking technology
executives.
Etude PwC : "Digital Banking Survey" (2014)PwC France
http://pwc.to/1jQNy0n
Le secteur bancaire ne doit cesser d'innover pour continuer de satisfaire les besoins de leurs clients au temps de la digitalisation. Retrouvez toutes les conclusions PwC sur ce sujet.
The term “alternative data” is tossed about in the industry, but what types of alternative data can truly be used when lenders want to make a credit decision? How can it be leveraged to help you grow your credit portfolio wisely? What insights can you glean to expand your consumer universe?
Uncover some of the latest trends attached to the non-prime universe and learn the latest around alternative credit data. This deck additionally explores how some of the newest attributes can benefit lenders of all sizes.
Our global data enables markets to be precisely sized and opportunities to be accurately gauged. We help our clients understand the consumer’s perspective, which we believe is critical to developing a successful product strategy in payments. Our team of consumer payments experts produces insight that provides answers to the questions you don’t know to ask yet.
P2P Lending Business Research by Artivatic.aiArtivatic.ai
Financial Lending or P2P Lending is going to play important role in the economy of entire world including India. Artivatic conducted Lending (P2P) research to understand the sector specific problems, growth and opportunities and also the use of technologies.
#lending #p2p #fintech #banking #insurance #payments #accounts #bfsi #deeptech #artivatic #startups #technology
This slide deck examines new product releases from credit card carriers in our Credit Card Monitor coverage group.
Inside, we provide a rundown of the new credit cards introduced on the firms’ websites during the first six months of 2015, and highlight their key features.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
2. Much of the lending industry has come to embrace the
value of comprehensive data for spotting new growth
opportunities. That’s why our most recent analysis –
Clarity’s 2018 Alternative Financial
Services Lending Trends Report –
is the perfect complement to
traditional credit reporting.
Our study analyzes the trends and financial
behavior of subprime consumers by looking at
application and loan data in our specialty credit
bureau from 2013 through 2017.
3. One of our biggest takeaways after
five years is that online loan volumes
continue to climb year after year.
39%
32%25%
2%2%
Silent Boomer Gen X
Millennial Gen Z
Index
Growth of Funded Loan Volume ($) – Online Installment Online Channel
4. Alternative credit data addresses a
number of lending opportunities, including:
How to respond to underserved
population segments
Insight into underdeveloped markets with
new options (e.g. alternative finance)
Parse data assets & engage consumers
5. New perspective with the most
comprehensive alternative credit data
Improve capability to predict consumer’s
ability to pay
Access to data not reported to national
credit bureaus
Fast-track path to compliantly
operationalize & scale new segments
6. Be predictive,
not reactive.
More lenders are discovering the
opportunities that exist in the
subprime credit demographic.
Now is your time to act!
Download the Report at
ClarityServices.com/2018Trends