Sheltered Harbor - Article from Connection

Few years ago, some of U.S. banks, including Citigroup, JPMC, and Bank of America, began working on a secret, ultrasecure data bunker called Sheltered Harbor. The data bunker holds a copy of all bank transaction data to protect it from a devastating cyberattack.

 

What is Sheltered Harbor? 

Sheltered Harbor is an initiative undertaken by the financial services sector. It provides an extra layer of protection against potential cyber risks. Sheltered Harbor is designed to provide enhanced protection for the customer accounts and data of financial institutions. Its goal is to securely store account data and to recover it even in the event of the loss of operational capability of a bank or brokerage.

 

Multiple industry associations collaborated to develop and deliver Sheltered Harbor. They include:

• American Bankers Association
• Credit Union National Association
• Independent Community Bankers of America
• Financial Services Forum
• Financial Services Information Sharing and Analysis Center (FS-ISAC)
• Financial Services Roundtable
• National Association of Federal Credit Unions
• Security Industry and Financial Markets Association
• The Clearing House

These financial services industry trade groups have established new resiliency capabilities to ensure that consumers will be able to access their financial accounts even if their banks or brokerages go out of business.

Banks has to pay from $1000 to $50,000 to become members of Sheltered Harbor. Members receive access to the full set of Sheltered Harbor specifications to ensure secure storage and recovery of their account data. 

Sheltered Harbor Provides Data Security

Sheltered Harbor provides data security through multiple mechanisms:

 

• It is physically isolated from unsecured networks. It has no connection to

    the Internet (it is air-gapped).

• It is redundant and decentralized.

• It can survive any attack or disaster because the vaults that store the banking   transactions are distributed geographically. Any disaster will leave at least one vault operational.

• It prevents data stored in its vaults from being changed by hackers or other unauthorized personnel.

• It is owned by each participant.

 

Customer data stored in a Sheltered Harbor data vault is encrypted and kept private by the institution owning that data. Extracted data is decrypted, validated, formatted, and re-encrypted before it is transmitted to the requesting party via industry-established file formats.

 

Sheltered Harbor establishes standards to increase the resiliency of participating institutions so that they can reliably access their data. It promotes the adoption of these standards and monitors the adherence of financial institutions to these standards so that consumers benefit from the added protections.

 

A Backup Buddy System 

Sheltered Harbor provides a backup buddy system. Banks choose ‘restoration’ partners that store a vault of one another’s core data, which is updated each night. If one bank goes down, the other can restore accounts from its buddy vault and make customers whole.

 

Thus, redundant backup vaults eliminate the risk of a single point of failure.

 

Each day, participating banks and brokerage houses convert customer data into a standardized format, encrypt it, save it in air-gapped storage, and put it in the air-gapped storage medium of their restoration partners.

 

Thus, the data is archived in secure vaults that are protected from alteration or deletion.

 

Sheltered Harbor is Complementary to FS-ISAC

 

FS-ISAC (Financial Services – Information Sharing and Analysis Center) is a U.S. industry trade group representing securities firms, banks, and asset management companies. It is the global financial industry’s resource for cyber and physical threat intelligence analysis and sharing.

 

FS-ISAC is a member-owned, non-profit organization. It was created by and for the financial services industry to help assure the resilience and continuity of the global financial services infrastructure against acts that could significantly impact the sector’s ability to provide services critical to the orderly function of the global financial system and economy.

 

Founded in 1999, FS-ISAC has over 7,000 members worldwide. FS-ISAC enables financial institutions to securely store and rapidly reconstitute account information should

the data become lost or corrupted. FS-ISAC makes account information available to customers in the event that an institution appears unable to recover from a cyber incident. In this respect, FS-ISAC performs functions similar to that of Sheltered Harbor and adds to the capabilities of Sheltered Harbor.

 

Summary

Sheltered Harbor was created to provide secure and resilient storage for the financial transactions of banks and brokerages. It is unique in that it is owned by the participating financial institutions.

 

Will Sheltered Harbor ever use blockchain technology to increase its security and resilience? A blockchain model has been created based on the Ethereum block chain. However, it has yet to gain approval by the participating financial institutions.

 

Check this site for more details : www.shelteredharbor.org

 

AI in Banking and Payments

• Banks are using AI on the front end to secure customer identities, mimic bank employees, deepen digital interactions, and engage customers across channels.
• Banks are also using AI on the back end to aid employees, automate processes, and preempt problems.
• In payments, AI is being used in fraud prevention and detection, anti-money laundering (AML), and to grow conversational payments volume.
  
   

Payments Based Analytics

Language Matters : Interesting Facts about few Terms

"Bankers" -   This word originates from the old Italian word banco, meaning table or bench, and refers to the fact that financiers once huddled in courtyards to conduct transactions with each other on those tables, in a tangible — relationship-based — manner.

"Payments" - It comes from the French word "Paier" which is used to mean appease.

"Corporation" : Comes from Latin word "Corpus" (body).

"Company"  - Comes from Latin word "companio" which means people breaking bread together.

"Technology" - Word comes from Greek Techne  which translates as skill, art or craft (such as weaving) combined with logos.

DBT(Direct Benefit Transfer) in INDIA through Blockchain

Governments since antiquity have tried to offer some level of welfare to the poor and these programs have expanded in modern times to truly large proportions. For instance, the supplemental nutrition assistance program (SNAP) in America costs approximately $71 billion a year (In 2016-17, GoI DBT payout was approx. 7.54 billion). Reducing the cost of administering these programs and increasing their effectiveness (by better targeting, eliminating fraud and corruption, reducing the number of intermediaries) has been a key challenge for all governments.

The use of technology has improved benefit administration considerably. We have gone from expensive paper tokens (US food stamps printed at the mint to minimize fraud) to electronic transfers straight to the beneficiaries’ bank account (India’s Direct Benefit Transfer scheme).

India’s implementation of Direct Benefit Transfer (DBT) has been a big step in eliminating a lot of the waste of earlier forms of benefit distribution. It not only reduces administration cost but by leaning on Aadhaar, the DBT system reduces both types of fraud – of ‘duplicates’ (a name getting benefits more than once) and of ‘fakes’ (benefits being taken in the name of a non-existent or fictitious person). These features alone make DBT far superior to other forms of benefit transfer.

DBT – Scope For Improvement

Despite the tremendous strides by DBT in eliminating waste, the system suffers from three important drawbacks.

1. Cash Flow Issues For Beneficiaries

While DBT has reduced the need for intermediaries, it has added cash flow burden for the end-beneficiary. So, for instance, in PAHAL (DBT for LPG), the customer has to first purchase the LPG cylinder and then the rebate is remitted to the bank account. This creates a cash flow burden for the beneficiary, which the Government has realized and is addressing through a new scheme – Ujjwala. However, creating new schemes creates new layers of awareness building and administration requirements.

2. Reliance on the Banking Network

Bill Gates famously said: “Banking is necessary. Banks are not.” However, the current DBT system has banks baked into its core as the system cannot work if the beneficiary does not have a bank account. The success of schemes like Jan Dhan Yojana not withstanding, it is worth asking if this reliance on banks is a good thing.

We would argue that it is not. There are real costs to using the banking infrastructure and it creates opportunities for banks to profit at the expense of poor customers. While not an issue yet in India, this has been a major challenge in the United States – where banks have been charging customers to even query their balance.

Furthermore, the cost of operating a bank account is estimated to be $5 per annum. Hence, the 310 million new accounts of the Jan Dhan Yojana cost about $1.5 billion to operate annually. Banks already finding ‘novel’ ways of paying for this cost – for example, by reintroducing the penalty for non-maintenance of minimum balance in its savings accounts. State Bank of India Chief Arundhati Bhattacharya said the public sector lender needs funds to balance the operational costs of Jan Dhan accounts.

3. Lack of Ongoing Transparency

For certain schemes, verification and transparency remain a challenge. For instance, schools are required to admit a certain number of backward and disadvantaged students. However, according to one study, seats are left empty owing to multiple inefficiencies: burden placed on parents, long enrollment process, schools disinterest in enrolling such students, and debt accrued owing to missing refunds. Furthermore, auditing and monitoring of these student enrollments in itself is a burdensome process for the government.

Lastly, one of the findings of the policy is that there needs to be constant monitoring to ensure outcome-based benefit transfer to align incentives of all stakeholders.

Similar verification and monitoring challenges exist across other welfare schemes as well.

A Blockchain-Based PAHAL System

PAHAL has been a hugely successful DBT program. However, currently, the beneficiary is required to pay for the cylinder and then the subsidy is paid to her bank account. She then needs to go to an ATM to access her funds.

The diagram below shows the current system in operation.

In a blockchain-based system, this delay in paying subsidy and need to visit the ATM would be eliminated. The diagram below shows how the system would work:

The token is a digital code that will be sent to the end consumer’s mobile phone number. This digital code will be tagged to the Aadhaar number using a mathematical hash function, thereby making every digital code unique to the Aadhaar number. The reconciliation is automated using the blockchain technology as both the government agency issuing the tokens and the oil companies redeeming the tokens are using the same distributed ledger.

The diagram clearly shows how the redesigned model using tokens is an improvement owing to simplification compared to the current as is model. In summary, the benefits are as follows:

1. Multiple banking transactions (from the government to consumers) are replaced by a handful of transactions (from the government to oil companies).
2. Tokens are tagged to individual Aadhaar numbers, hence reconciliation is exponentially simplified.
3. All transactions – issuance and redemption of tokens – are stored using the blockchain technology, thereby providing transparency and audibility.

The same concept can be expanded to any DBT system. Exact token features and blockchain features will differ from case to case, but at the heart of it, they remain the same.

Governments like the UK are already experimenting with blockchain-based benefit transfer. In India, due to Aadhaar, we have the opportunity to be a leader in the blockchain space as well. It’s time to bring benefit payments to the 21st century and for India to lead that implementation.

EU Banks Are Using AI as a Tool for Transformation

Similar to the largest US institutions, European banks have a strong interest in exploring the impact of AI on business functions. The US market, however, seems to be far more active in areas beyond virtual assistants and chatbots, while European banks are heavily focused on customer-facing interfaces. In fact, a study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor.

Banks are using chatbots and voice bots to interact with customers and resolve requests before a human intervention is required. Fortunately, the technology behind it – natural language processing and generation – will make it increasingly difficult for customers to tell whether they are talking to a human or an AI interface. Biometrics, particularly voice and face, could be used as authentication methods to ensure secure interactions.

What else are European banks doing with AI?

Virtual assistants, chatbots

To mention a few examples – Russia-based Tochka Bank has launched a Facebook bot for a range of financial services that include allowing the bank’s clients to check their accounts, finding nearby ATMs using geolocation, calling the bank function, contacting customer support, and making payments via Facebook messenger.

Another major institution has integrated IBM Watson and has designed a customer service chatbot – a natural language processing AI bot to answer customer’s questions and perform simple banking tasks like money transfers. If the bot is unable to find the answer it will pass a customer over to a member of staff.

A London-based bank has deployed a text-based chatbot, which customers can use on the banks’ online help pages. The chatbot can answer 200+ basic banking queries.

Automation

With RPA expected to have a $6.7 trillion global economic impact and a global market potential standing at $8.75 billion by 2024, most institutions in the US have work planned to bring automation across functions. Europe also has its examples – a securities services function for a major institution in Europe is employing a trade-matching tool using artificial intelligence and predictive analysis to further automate the trade processing services it provides to investment managers.

Using predictive analysis, the solution analyzes historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. The bank is already making good progress, having reached around 98% prediction accuracy.

Compliance, Fraud Prevention, AML

A tool developed by one of the largest European institutions systematically screens contracts for compliance purposes. It takes 15 seconds to screen 150 pages, and the tool makes it possible to identify the names of legal entities, people, locations, vessels, etc.

HSBC, for example, is bringing in robots to help it spot money laundering, fraud, and terrorist funding. The bank is planning to integrate the AI software of Quantexa, a UK-based startup, to screen the vast amounts of data it holds on customers and their transactions against publicly available data, in the search for suspicious activity.

The same institution is also using AI to predict how customers might redeem their credit card points so it can market its rewards offerings more actively and effectively. The rewards program will read customer data to predict how they might redeem their credit card points so it can market the offerings of a certain category – travel, merchandise, gift cards or cash – more actively.

Another European bank has partnered with a startup and implemented its enterprise analytics solution using AI to better identify instances of fraud while reducing false positives. Through this, the bank reduced 60% of false positives and increased true positives by 50%.

ING Bank went ahead with replacement of a traditional rules-based anomaly detection system with one powered by machine learning algorithms. Previous testing has shown this will improve performance significantly – much more than the 5%-10% typically attained in a technology upgrade.

Trading & Investments

One of the largest European banks launched an artificial intelligence bond trading tool that will help human traders to swiftly gather better bond prices. The tool will use data from hundreds of thousands of trades to help the bank’s traders to get better bond prices faster. In a six-month trial, the AI tool led to faster pricing decisions for 90% of trades and cut trading costs by 25%.

UBS, for example, is working on solutions using machine learning to develop new strategies for trading volatility on behalf of clients. It scans vast amounts of trading data and creates a strategy based on learning from market patterns. The strategy, however, has then to be approved by human employees. The bank is also developing an AI tool for investment research. It can screen through market data through SEC filings and can actually do a company valuation with all of the inputs that a human analyst would use and can produce text in a fairly decent quality and almost human-mimicking language.

Deutsche Bank has rolled out new AI-based equities algorithmic platform in APAC, which was designed with a self-learning mechanism allowing its systems to predict equities pricing and volume with more accuracy, thereby enhancing the quality of execution. This was added as a capability to its Autobahn platform.

“Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry.” – How Banks Are Using AI as a Tool for Transformation

CHAT POT - Architecture

Fintech Glossary

World Economy in a chart

Innovation in Banks

Difference Between Design Thinking and Traditional Thinking

Artificial Intelligence

Banks Challenges VS Fintechs Chances

Different Types of Fintech

How 3D secure 2.0 supported by Riskshield

Riskshield - Fraud Detection Tool from Inform

RiskShield provides a reliable, fast and responsive anti-fraud solution to meet these requirements and protect your organization against financial crime and cyber attacks. 

Riskshield offers a flexible and highly configurable risk assessment and fraud prevention solution which monitors variety of products and channels in Banking, Payments and Cards Processing Services.

Advanced analytics and Human intelligence based technology

A supervised learning solution using patented fuzzy logic technology and intelligent profiling.

RiskShield delivers unique human like decision technology with a large variety of complementary techniques like machine learning, fuzzy controlled rules, pattern recognition, data-mining, network detection and mathematical algorithms to ensure the best mix in fraud detection effectiveness.

Real-time and High performance detection engine

A powerful detection engine with in-memory processing allowing performances under 20 milliseconds.

RiskShield Server operates as a flexible and highly configurable monitoring and prevention decision engine, optimized for high performance environments and designed to operate in real-time or batch mode with 24x7 on-demand.

User friendly and intuitive rule management

A powerful tool to create, analyze and simulate fraud detection rules

RiskShield Client offers a secure and user-friendly interface to develop, maintain, analyze and optimize fraud detection rules in production without downtime. RiskShield Client also provides multiple analysis functionalities to improve scoring results as well as simulate and conduct tests with live production data. The graphical presentation of fraud prevention rules allows easy verification, modification and adaptation to new fraud patterns and can be modified by in-house fraud experts without internal IT support

Case investigation and alert workflow management

A complete case management tool for case investigations and alerts

RiskShield Investigator is a case management tool which offers users the flexibility to automate activities and processes across the complete life cycle of a fraud case. The solution provides the basic framework that defines processes for researching and resolving cases, including investigation resources, time frames, escalation paths and alerts. Acting as a central repository for case activities, RiskShield provides a complete history and fully centralized audit trail on all aspects of case investigations.

Business Intelligence and predictive analytics

A visual analytics, dashboard and reporting solution

RiskShield Business Intelligence helps financial service providers and insurers to access greater insight into transaction flows, fraud cases and customer portfolios, using an intuitive, user-friendly interface to analyze any available data in a graphical and visual display. By delivering information in dashboards as well as scheduled or on-demand reports, fraud managers are able to look at the information in different ways.

Confronting the Greatest Risks Facing The Future of Banking from Financial Brand

Few Risks which I see in Industry. Its NOT just in IT , Banking. Its applicable everywhere.

1. Risk of Complacency
2. Risk of Current Success
3. Risk of playing to NOT lose
4. Risk of being a just Banker

"If you stay in the safety of complacency without a notion as to what’s happening in the company or in your industry, your safety zone can become a danger zone overnight"

According to Sonia Wedrychowicz, Managing Director and Head of Technology Transformation at JPMorgan Chase
“If you are successful and comfortable – don’t allow yourself and your company to fall into the trap that it will last forever … that’s the best time to transform and change!”

According to the book, “Top Dog”, by Po Bronson and Ashley Merryman, “Competitive fire will never ignite, or be expressed, when our orientation is just to get through the day. Competitive fire will flourish when long-term goals are high, and when it’s accepted that risks and mistakes go hand-in-hand, and we are free to let ambition reign.”

From my perspective, I have seen that “bankers being bankers” tends to result in 
- lower acceptance of change; 
- an adherence to legacy policies, processes, and thought patterns; 
- and the resultant risk of not being able to keep up with consumer demands.

What is Strategic Thinking?

Understanding Strategic Thinking

Many of us, have misconceptions about what constitutes strategic thinking. I don't think you'll find a generally accepted textbook definition of the term, so I'll put my stake in the ground and make this assertion:

Strategic thinking is more about looking at the past and present than it is the future.

Strategic thinking is introspection, not necessarily forecasting. Strategic thinking is asking:

• What's working and not working--and why or why not?
• Why do consumers do what they do, or not do what they don't do?
• Why does that one pesky competitor of ours always eat our lunch?
• What assumptions about the business do we hold dear, but not might be true? (think bank branches)

While I'm sure there are others, there are two common barriers to strategic thinking:

• Some aren't equipped to address those questions. Reality is, many of executives, got to where they are because they're really good at managing people and/or operations. When they get to the senior ranks, there's this unwritten rule that somehow--magically--they're going to turn into strategic thinkers. Doesn't work that way. Sorry.
• The truth hurts. Here's an uncomfortable thought: Maybe the reason things aren't working out like planned, or why that competitor beats you every time is the result of decisions YOU made or actions YOU took. Sure don't want that coming to light in the strategic planning process, do you?

Digital Transformation Pyramid

CLOUD TRANSFORMATION DETAILS IN Banking and Financial Industries

Cloud-based services are known to be driving operational efficiency and significantly decrease costs of running any business. With financial services, however, the journey to the cloud has been more complicated than for other industries due to a variety of reasons, risk management and security concerns being some of them. Nonetheless, the evolution of IT infrastructure is underway and financial services companies are aggressively exploring opportunities.

Large financial institutions around the world have turned to cloud services for a variety of purposes and found the move to be highly advantageous. One of the largest banks in Spain, Bankinter, for example, is using AWS to run credit risk simulations in 20 minutes, down from 23 hours before. For the Commonwealth Bank of Australia, the cloud has reduced the time and cost of standing up a new server from eight weeks and several thousand dollars to eight minutes and 25 cents, making the bank much more responsive to changing customer demands.

CLOUD TRANSFORMATION DETAILS
InvestLab, based in San Francisco and Hong Kong, is a financial services technology company focused on the global trading market. InvestLab uses AWS for front-end connectivity for brokerage administration, trading systems, market data, and InvestLab products. The company uses Amazon Elastic Compute Cloud (Amazon EC2) instances in the US East, US West, and Asia Pacific-Singapore regions, and employs Elastic Load Balancing and Amazon Relational Database Service (Amazon RDS) to support InvestLab cloud server instances. InvestLab realized a 40% reduction in the fixed cost of launching a software product. “AWS saved us hundreds of development hours, which put us eight to twelve weeks ahead of schedule. Now we can execute and realize a more aggressive product development strategy,” commented Tim Reynolds, VP of Information Technology at InvestLab.
Nubank, a FinTech bank based in Brazil that offers a no-fee, low-interest credit card that customers can manage with their iOS and Android devices, used AWS to build, deploy and run its credit card processing platform on which customers can track and control their purchases. By using AWS, Nubank developed its credit card processing platform in only seven months and can add features with ease.
Ohpen : A Dutch company that provides banks with a modular platform for administering retail mutual funds and savings accounts for consumers, have deployed their solution entirely on the AWS cloud. The decision allowed them to deploy new features in three months or less, compared to a year or more using traditional IT, and company leaders estimate that their institutional customers can save up to 80% in IT costs by using the Ohpen platform in the cloud.
Intuit : A leading provider of financial management software for consumers, small businesses, and accounting professionals, moved its TurboTax AnswerXchange application to AWS. As a result, Intuit was able to reduce costs by a factor of six because it no longer had to maintain idle servers for an application that was only active during tax season. After this first success, Intuit subsequently moved 33 applications, 26 services and eight enabling tools to the AWS Cloud. Over the coming years, Intuit will move the rest of its applications to AWS to speed development, innovate faster, and better solve customers’ needs.
eFront : A French software company that provides solutions for the financial industry across 21 international markets uses AWS to host a virtual private cloud for its customers. By using AWS, eFront has been reported to reduce costs and improve time to market. The company also plans to use AWS to expand its services for both internal and external clients.
Federal Home Loan Bank of Chicago : A $70-billion wholesale bank that lends money to other financial institutions to support liquidity in the real estate market. The organization began its journey to the cloud by migrating its analytics and disaster recovery solution to AWS. Today, the organization runs all of its internal production workloads on the cloud and, as a result, has lowered costs by 30%.
MEBank : Melbourne Bank, which manages $20 billion in assets and has 800 employees who support 280,000 customers around Australia, uses Amazon VPC to provision an isolated, virtual network in the AWS Asia-Pacific (Sydney) Region. Using AWS instead of an on-premises datacenter infrastructure allowed ME Bank to accelerate the provisioning of development and testing environments by up to six weeks. The company was able to reduce the cost of delivering development and test environments for new applications and services by 75%.
mCASH : Norwegian payment provider and e-money institution licensed in accordance with the EU payment services directive, relies on Google App Engine, a service of Google Cloud Platform, on the backend. mCASH plans to integrate additional Cloud Platform products as the business and platform mature.
Mambu : Banking technology startup, which helps banks, microfinance institutions, and other financial innovators deliver essential banking services to individuals and emerging enterprises around the world and powers the services behind 2.2 million end-user accounts is also using cloud services. Mambu runs all its services on AWS, from development to production. It deploys code using AWS Elastic Beanstalk, which distributes the application across more than 200 Amazon Elastic Compute Cloud (Amazon EC2) instances. The fully managed Amazon Relational Database Service (Amazon RDS) serves as Mambu’s main database, while Amazon ElastiCache synchronizes session information across servers. Using AWS services allowed Mambu to reach better availability and flexibility to power intensive growth across regions.
WorldBank : The organization was reported to begin moving more to the cloud: Microsoft apps like Office 365 and SharePoint will be on Azure, most of the rest will move to AWS. One result of shifting work to the cloud is that the bank is moving from five data centers to two, and probably to one eventually. Systems for financial reporting, which have strong internal controls, will stay on-prem for longer. “The IT group has shed 10 to 15 percent of its headcount, repositioning many staff positions and cutting a lot of contractors. The run rate to manage Lotus Notes was $12 million and now it costs us $4 to $5 million with Office 365, and we went from 20 FTEs to 6. Many of our staff are here on special visas so we try to retrain and reposition,” commented Stephanie von Friedeburg, The World Bank CIO.
NAB’s Global Equity Derivatives Group (GED), which provides stock-trading solutions that manage exchange-traded securities such as stocks, funds, futures, and options, partnered with TickSmith, a Canadian software provider specializing in big data management and analysis technologies for financial data, which runs on AWS. GED is able to easily scale TickVault to consume and analyze financial data. The organization’s business analysts conduct post-trade analysis much faster than before. With AWS, data manipulation, processes that took days have been brought down to one minute. The post-trade analysis that used to take weeks is done in just a few hours with the ability to look at both current and historical data.
FIS : A global leader in financial services technology, runs US market analysis using the Google Cloud Platform. FIS’ Market Reconstruction Platform can collect, link and store data on every equity and options trade lifecycle event and then produce feedback reports within a few hours. Its high-performance system can adjust to fluctuating market activity and support complex analytics. FIS also relies on Google Cloud Dataflow to quickly process, format and validate incoming data before sending it to Google Cloud BigQuery for analysis. Cloud Dataflow provides FIS with a managed services environment that supports batch and stream data processing, which allows the FIS team to focus on data processing tasks, instead of cluster management.
Canaccord Genuity, an independent full-services financial firm, uses Google Cloud Platform to manage an information platform for professional investors and business analysts. The platform, called Quest, uses Google BigQuery to pull up and cross-reference over 9,000 companies, using a Google Sheets-based decision tree that defines more than 10,000 rules. Instead of a classical three-tiered architecture, the new Quest® system has no application server, which simplifies the design and improves performance. Google BigQuery drives the data analysis, grinding through over 100 million rows in only a few minutes, at speeds at least 100 times faster than the previous system. Quest also uses Google Cloud Storage, since a typical analysis run generates more than 200 GB of data, detailing all the cross-references, trend tables and other information comparing companies against each other and against relevant industrial sectors.
JPMorgan Chase’s Chief Operating Officer Matt Zames has been reported to say that the bank is contemplating the use of AWS for spiky workloads—say, credit card transactions that take place on Black Friday.
Temenos, a global banking software provider, has developed its new version of software on the Microsoft Azure platform, which allowed the company to offer cloud banking capabilities to companies that have traditionally used on-premises solutions. It has helped them meet strict security and compliance requirements. As a result, banks using T24 in the cloud can deploy the application in only a few months, meet security requirements, and meet close-of-business deadlines faster. Online bank Simple uses AWS to run its virtual banking platform and meet payment card industry (PCI) data security standard (DSS) compliance for its development and production environments. By using AWS, Simple automated processes that once took months to complete and instead focus on its customer service rather than managing IT infrastructure.
CardFlight, one of the leading providers of tools and technology that allow developers to build their own mPOS, chose to host its supporting IT infrastructure on AWS cloud in order to minimize the burden of PCI compliance and bring its mobile EMV payment platform to market as quickly and cost-effectively as possible. A number of AWS cloud technologies help CardFlight to streamline PCI compliance, including AWS Key Management Service (AWS KMS). To protect sensitive data stored in AWS, CardFlight uses Amazon Virtual Private Cloud (Amazon VPC). “Based on an informal analysis, I’d say that both our capex and opex costs are around 40 percent lower with AWS compared to building out infrastructure in traditional data centers,” commented Jesse Angell, Software Engineer at CardFlight. “Operating on AWS means we spend less on infrastructure. This is a great benefit as we are then able to spend more on product development, bringing more value to our customers than we could have otherwise.”
Capital One, one of the nation’s largest banks, is using AWS in hope to reduce its data center footprint from eight to three by 2018. The bank is using or experimenting with nearly every AWS service to develop, test, build, and run its most critical workloads, including its new flagship mobile-banking application. “There’s nothing we aren’t willing to put in the public cloud,” said Rob Alexander, Capital One’s Chief Information Officer. “We are now doing the vast majority of all our new development in the public cloud, and we are systematically moving our legacy applications.”