If you said “too much time,” you’re right. According to a Deloitte report, finance teams spend 48% of their time creating and updating reports. And when they’re operating at such a tactical level, it can be hard for them to see the forest for the trees.

Without a modern approach to financial reporting, finance teams are so bogged down in the details that they simply don’t have the time to uncover insights in the data 一 insights that could be vital to your business.

So how do you help them? In this piece, we’ll highlight five things you need to strengthen your financial reporting and be strategic in the data decade and how you can get them.

Amazon overtreft winstverwachingen dankzij cloud

Amazon heeft dankzij een sterke groei in zijn cloud computing-afdeling betere kwartaalcijfers gepresenteerd dan verwacht. De omzet van het bedrijf steeg met 17% naar 59,7 miljard dollar (53,5 miljard euro). De nettowinst kwam uit op 3,56 miljard dollar, wat ruim twee keer zoveel is als de 1,6 miljard dollar een jaar eerder. De operationele inkomsten werden ook ruim verdubbeld naar 4,4 miljard dollar. 

Daarmee zijn de verwachtingen van Wall Street ruim overtroffen volgens Silicon Angle. Analisten hadden gerekend op een nettowinst van 2,3 miljard dollar met een omzet van 59,65 miljard dollar.

Cloud

De meeste winst kwam opnieuw uit de cloud-afdeling van het bedrijf. De omzet uit de cloudsteeg met 41% tegenover een jaar eerder naar 7,7 miljard dollar. Dat is net iets meer dan analisten verwacht hadden. Het operationele inkomen steeg met 59% naar 2,22 miljard dollar.

De omzetgroei van de afdeling is iets vertraagd ten opzichte van het vierde kwartaal, toen de groei nog 45% was. Volgens Chief Financial Officer (CFO) Brian Olsavsky is dat deels te wijten aan een erg goed kwartaal voor Amazon Web Services (AWS) vorig jaar. Daardoor zijn de vergelijkingen moeilijker.

Daarnaast blijven de zaken op dit gebied volgens Olsavsky altijd wat moeilijker te voorspellen, omdat er onzekerheden bestaan over hoe snel bedrijven AWS en de cloud adopteren en operaties migreren vanuit hun eigen datacentra. Het klantgebruik van AWS-diensten blijft echter meer dan de omzetgroei.

Advertenties

Niet alleen de cloud-afdeling, maar ook de advertentie-afdeling van het bedrijf zag een flinke groei. Het “other”-segment van Amazon, dat vooral uit advertentieverkopen bestaat, zag de omzet met 34% stijgen naar 2,72 miljard dollar. Amazon is daarmee nu de derde grootste verkoper van digitale advertenties, achter Google en Facebook.

Voor het huidige kwartaal heeft Amazon zijn verwachtingen bijgesteld. Het bedrijf verwacht een omzet tussen de 59,5 miljard en 63,5 miljard dollar, wat overeenkomt met de verwachting van Wall Street van 62,4 miljard dollar. Het verwachtte operationele inkomen tussen de 2,6 miljard en 3,6 miljard dollar is echter lager dan de verwachtingen van Wall Street. Die verwachten een operationeel inkomen van 4,19 miljard dollar.

Bron: Techzine

How Big Data leaves its mark on the banking industry

Did you know that big data can impact your bank account, and in more ways than one? Here's what to know about the role big data is playing in finance and within your local bank.

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. These terms are fundamentally tied predominantly to matters involving digital transformation as well as growth in companies. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved.

Through this tool, gaining better insight has become much easier now. It doesn’t matter whether the decision being considered has huge or minimal impact; businesses have to ensure they can access the right data to move forward. Typically, this approach is essential, especially for the banking and finance sector in today’s world.

The role of Big Data

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. They have to utilize Big Data to its full potential to stay in line with their specific security protocols and requirements. Banking institutions actively use the data within their reach in a bid to keep their customers happy. By doing so, these institutions can limit fraud cases and prevent any complications in the future.

Some prominent banking institutions have gone the extra mile and introduced software to analyze every document while recording any crucial information that these documents may carry. Right now, Big Data tools are continuously being incorporated in the finance and banking sector. 

Through this development, numerous significant strides are being made, especially in the realm of banking. Big Data is taking a crucial role, especially in streamlining financial services everywhere in the world today. The value that Big Data brings with it is unrivaled, and, in this article, we will see how this brings forth positive results in the banking and finance world.

The underlying concept 

A 2013 survey conducted by the IBM’s Institute of Business Value and the University of Oxford showed that 71% of the financial service firms had already adopted analytics and big data. Financial and banking industries worldwide are now exploring new and intriguing techniques through which they can smoothly incorporate big data analytics in their systems for optimal results.

Big data has numerous perks relating to the financial and banking industries. With the ever-changing nature of digital tech, information has become crucial, and these sectors are working diligently to take up and adjust to this transformation. There is significant competition in the industry, and emerging tactics and strategies must be accepted to survive the market competition. Using big data, firms can boost the quality and standards of their services.

Perks associated with Big Data

Analytics and big data play a critical role when it comes to the financial industry. Firms are currently developing efficient strategies that can woo and retain clients. Financial and banking corporations are learning how to balance Big Data with their services to boost profits and sales. Banks have improved their current data trends and automated routine tasks. Here are a few of the advantages of Big Data in the banking and financial industry:

Improvement in risk management operations

Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. It improves the response timeline in the system and consequently boosts efficiency. Big Data provides financial and banking organizations with better risk coverage. Thanks to automation, the process has become more efficient.Through Big Data, groups concerned with risk management offer accurate intelligence insights linked to risk management.

Engaging the workforce

Among the most significant perks of Big Data in banking firms is worker engagement. The working experience in the organization is considerably better. Nonetheless, companies and banks that handle financial services need to realize that Big Data must be appropriately implemented. It can come in handy when tracking, analyzing, and sharing metrics connected with employee performance. Big Data aids financial and banking service firms in identifying the top performers in the corporation.

Client data accessibility

Companies can find out more regarding their clients through Big Data. Excellent customer service implies outstanding employee performance. Aside from designing numerous tech solutions, data professionals will assist the firm set performance indicators in a project. It will aid in injective analytic expertise in multiple organizational areas. Whenever there is a better process, the work processes are streamlined. The banking and financial firms can leverage improved insights and knowledge of customer service and operational needs.

Author: Matt Bertram

Source: Smart Data Collective

How Machine Learning is Taking Over Wall Street

Well-funded financial institutions are in a perpetual tech arms race, so it’s no surprise that machine learning is shaking up the industry. Investment banking, hedge funds, and similar entities are employing the latest machine learning techniques to gain an edge on the competition, and on the markets. While the reality today is that machine learning is mostly employed in the back office–for tasks such as credit scoring, risk management, and fraud detection–this is about to change dramatically.

Machine learning is migrating to where the action is: financial market trading. Once leading-edge Wall Street platforms that companies invested many millions in are soon to become obsolete due to machine learning. Understanding how disrupting Wall Street will change and evolve and why it matters is key to navigating the opportunities ahead.

Algorithm Trading

Algorithmic trading now dominates the derivative, equity, and foreign exchange trading markets. These trading strategies can be complex, but the essentials are straightforward: program a set of rules that takes market data as input and apply basic models (10 -day moving average) to generate an automated trade workflow. Over the years, these strategies have moved beyond simple time-series momentum and mean revision models to more exotic name strategies like snipes, slicers, and boxers. Evolved over decades, algorithm trading has replaced much of the manual trade order flow with faster static rules-based strategies. What was once cutting edge is now an inherent disadvantage. Static rules, no matter how complex, may work well in relatively stable markets but can’t react to evolve rapidly changing market conditions.

A machine learning algorithm’s clear advantage is it learns from experience and is not static. Employing massive datasets and pattern recognition, these algorithms produce models that learn from experience and are orders of magnitude more powerful than old-school algorithmic trading models. Decisions on how and when to trade will be made in some cases by using multi-agent systems that can act autonomously. At some point, these static algorithms will be no match for more nimble machine learning algorithms.

Why it Matters: Reskill and Upskill

Companies that make use of algorithm trading need to reskill or risk getting left behind. In a winner-take-all market, companies employing only slightly more advanced techniques like machine learning will continuously win a bigger share of the market. In addition to machine learning, businesses should expect an increased demand for data engineers, data scientists, MLOps specials, and others that can handle this sophisticated workflow.

High-Frequency Trading Agents

High-frequency trading (HFT) is the flashy cousin of algorithmic trading. Employing similar rules-based models, or even predictive analytics, these strategies operate at a much more rapid pace,; completing hundreds of stock trades in nanoseconds versus longer time range algorithmic trading strategies. High-frequency trading also relies on massive hardware and bandwidth infrastructure investment that often requires system colocation next to major exchanges. Given its sophistication, only 2% of financial trading firms employ high-frequency trading, yet at its peak, it accounted for 10 to 43% of stock trading volume on any given day.

The ingredients for HFT–massive computing power, high frequency streaming big data, and ultrafast connections–are all areas where deep learning and machine learning workflows excel. 

Pre-trained models can prevent machine learning and deep learning algorithms from becoming speed-limiting factors. Coupled with techniques such as deep reinforcement learning, HTF is primed for another technological leap. However, given its increased complexity, it will remain the domain of a relatively few, but highly profitable, firms. 

Why it’s Important: Trouble Ahead

The Flash Crash that occurred on May 6, 2010 caused trillions of dollars of market equity to be wiped out in an instant (36 minutes to be precise). Regulators have struggled to keep up with algorithmic trading and high-frequency trading, and will doubtless be hard-pressed to stay ahead of the next generation. HTF AI agents will require much more sophisticated risk monitoring and compliance systems that in turn will need to employ machine learning to monitor.

Risk Assessment Platforms

Despite the vast sums invested in technology by financial institutions, the humble Excel spreadsheet remains the number one applicationon Wall Street. Risk departments, charged with ensuring traders don’t make calamitous errors, are no exception. Even the better-equipped firms employ software that relies on rule sets and analytics that are apt at catching known risks but are poorly equipped to identify evolving market risk.  

The nature of a robust risk assessment platform is a kind of catch-all. Risk scales from individual trades, to companies, industry, country, and global risk profiling. Risk can be quantifiable, but often a risk assessment may need to rely on alternative data. Machine learning’s adaptability and flexibility make it a natural successor to current risk assessment software. Both supervised and unsupervised machine learning techniques can be employed to layer on more sophisticated risk strategies. Anomaly detection used to identify outliers is one such technique that can be readily employed to identify the rare events that are characteristic of risk modeling.  

Why it Matters: Risk and Repeat

The recent implosion of Archegos Capital in March cost some of the world’s most sophisticated banks to lose up to $10 billion, highlighting the poor systems and oversight that many financial institutions face with have to trade risk exposure. Similar risk failure, albeit of a smaller magnitude, continues to abound despite the lesson learned and trillions lost due to the risk failure that gave rise to the 2007 financial crisis. Risk departments are finally waking up to the inherent advantages of pattern recognition machine learning versus manual and backward-looking analytics tools. Add to this the increased complexity due to, you guessed it, machine learning trading strategies.

OMS Trading Platforms

Retail traders have flocked to online trading platforms like Robinhood, Fidelity, and E*Trade. The institutional professionals use more advanced systems called OMS (order management systems) from companies like B2Broker, Charles River, Interactive Brokers, and others. These institutional trading platforms all execute the same basic workflow. Financial market data is fed in; a set of static trading, risk, and compliance rules are applied; buy and sell orders are generated; the order book is updated, and trade analytics reports are generated. 

Traditionally, these platforms were closed systems. Many provide limited APIs that allow customization of various aspects such as data feeds, order flow, and algorithms, but most work only within the confines of their particular platform. Advanced hedge fund traders are employing sophisticated machine learning and deep learning techniques that utilize platforms like Tensorflow, Keras, PyTorch, and similar frameworks and libraries. Deep learning techniques such as deep reinforcement learning, NLU (natural language understanding), and transfer learning require these platforms. These models often require alternative data whose unstructured format does not readily make itself suitable for the structured time-series format many of these present trading platforms require. 

Why it’s Important: From Closed to Open

At some point, this equation will flip. Trading platforms are very good at order workflow and trade analytics. However, data profiling, data transformation, and machine learning algorithms need something much more flexible, adaptive, and open. The existing dominant market players will need to adopt a more open API approach that gives full access to every stage of the order workflow. At some point over the next 5 years, this in turn will lead to adoption by retailed brokers and bring machine learning trading to the masses. 

From Leader to Laggard

For the last few decades, Wall Street has been a clear leader in rolling out complex platforms such as algorithm trading and high-frequency trading (HTF), and other innovative trading strategies.  However, many of these systems rely on static rules-based systems or predictive analytics at best. Other companies that fully embraced machine learning and deep learning earlier have come to dominate sectors of their industry. Expect a similar shakeout in financial institutions as some companies go all-in on artificial intelligence and become the next generation of technology leaders.  

Author: Sheamus McGovern

Source: Open Data Science

Learning from the financial reports of your business: 3 important questions to ask

Your business’s financial reports should help you make important business decisions. While there are many smart decisions you’ll be able to make with efficient reports by your side, these are three of the most important questions to ask:

1. What should I sell more or less of?

A key principle to help you, not only to make your business survive but to make it flourish, is to use the market intelligence you have and to provide for your market's current and future needs and wants. By using sales reportst hat show your top selling and least selling products over a certain period, you can easily identify which products you need to:

• a) Market differently
• b) Tweak so that they become more customer-friendly
• c) Cut down on producing or sourcing, or
• d) Discontinue altogether.

Sales reports can also reveal top customers. This enables you to spend more time focusing on relation ship management with your essential customers, ensuring that these customers are well taken care of. You can then redirect the cash you generate from these customers into market aspects that need improvement.

2. Do I need additional funding?

There are a few internal courses of action you could look at first, like reducing costs or driving more sales to increase revenue for example. But this may not be enough. Many start-up and small businesses are self-funded, which is ideal. However, for many businesses there comes a time when cash flow is not sufficient to maintain operations and additional funding may be necessary. Whether it’s acquired through a business loan or through an external investor, financial reports are required to convince a bank or an investor that you can manage your finances. So you need to ensure that you keep track of your finances regularly and that your financial data are always up to date. Financial reports such as the income statement and can help you gauge whether you need additional funding to curb seasonal fluctuations, or whether you need to purchase additional stock to cater for a growth in demand.

3. How and when can I expand my business?

When your business has been profitable for some time, you don’t have to stress about cash flow, and the demand for your product exceeds your expectations, it’s a clear sign that business is going well. This means that it may be time to expand your business. As exciting as expansion is, it needs to be considered very carefully. In this consideration,  insights from financial reports and other business reports will be crucial. There is also a sum of other options you could consider before expanding. Examples are: replacing old equipment, settling debts, giving something back to shareholders, or putting money aside for a darker period. However, if your mind is made up on expansion, a big decision is to decide on what type of expansion to go with. Possible expansion options are:

• Sell more of the same products.
• Open a shop in another area.
• Diversify – offer complimentary products to what you currently offer.
• Franchise your business.
• Merge with or acquire another business.

Not all expansion options are suitable for all types of businesses or industries. It’s important to do thorough research into what will work for you and your business, and what resources you have at your disposal. Use all relevant intelligence that is available! Part of this research will be determining the long-term profitability of your business, considering the expansion opportunity. What does the future look like? Without having access to up-to-date financials that you can easily analyze, you could end up making rash decisions that could have negative consequences or even cost you your business. Being able to make quick decisions, based on accurate data, allows you to kunderstand whether you need to wait for the right moment to expand your business, or if your business is ready to do it immediately. Whatever the case, realizing and understanding the importance of your business’s financial reports goes a long way to growing and maintaining a successful business. We hope you keep these insights in mind and can become the champion of your financials and get them to work for you and your business.

Author: Milentha Bisetty

Source: Sage Intelligence

Vast amounts of information are being sifted for the good of commercial interests as never before

IBM’s Watson supercomputer, once known for winning the television quiz show Jeopardy! in 2011, is now sold to wealth management companies as an affordable way to dispense investment advice. Twitter has introduced “cashtags” to its stream of social chatter so that investors can track what is said about stocks. Hedge funds are sending up satellites to monitor crop yields before even the farmers know how they’re doing.

 

 

Banks are among the biggest investors in big data

 

 

The artificial intelligence used in driverless cars could have applications in finance

The biggest challenges for AI in finance

Education, explainability, privacy and integration are some of the problems institutions face when implementing machine learning tools and technology.

From credit cards to title insurance to loans and even fraud and risk management, the finance industry is making use of AI tools and technologies. But implementing these tools and working through their inherent challenges has proved difficult for financial institutions.

That was the sentiment shared by major credit card providers, insurance companies and banks during the Ai4 2022 Finance Summit here on March 1. The application of AI tools in finance is necessary for many institutions, and many plan to increase their budget to continue to implement the tools.

Sixty-five percent of 706 senior IT professionals in the finance sector plan to increase their IT budget in 2022, according to a November 2021 report by Enterprise Strategy Group.

Among that increase, 62% of respondents say they will likely increase spending in artificial intelligence and machine learning.

However, since finance involves sensitive data from consumers and large corporations, some enterprises find themselves trying to find a balance between the benefits and risks of AI tools.

The first problem for companies using AI technologies in finance is a lack of education, said Priya Rajan, CMO at DataVisor, during a panel discussion about AI and credit cards at the conference.

DataVisor, a fraud and financial crime detection company, uses AI and machine learning to identify fraud attacks. But, Rajan said, so much is still unknown about AI that it's difficult to identify what's true AI versus what's not.

"Education is such a big part of this transformation in this industry and I do expect that to continue in the next decade because we're just scratching the surface of what the technology is and what it can do," Rajan said.

AI challenges in finance

Another challenge is explainability, said Clay Jackson, vice president of product management for small business cards at Capital One, during the panel discussion.

Credit card providers are put into tricky situations where not giving more credit to someone might mean that person can't pay for funeral fees or get a job.

"We're taking actions that impact customers' lives," Jackson said.

When a customer is denied credit, institutions must be sure why they're saying no, Jackson said.

"I feel like the explainability problem slows us down," he said. "And for the right reasons."

AI tools also pose privacy and bias challenges to the credit card sector, said Rick Ballmann, vice president of engineering, data intelligence and customer experience at American Express, during the same panel discussion.

While banks and financial institutions can offer consumers loans based on where they currently spend their money, it doesn't mean they should, Ballmann said.

"It's like knowing the boundary between going too far and what the customer would appreciate as good customer service," he continued. "It's not exactly what's possible, [but] what's the right thing to do for the customer."

The use of AI tools in fraud detection also presents data privacy issues, said Besa Abrashi, senior project manager at American International Group (AIG), a finance and insurance company.

As someone who works on AIG's fraud detection team, she said there are different data privacy regulations and procedures that must be followed when exchanging data from one place to another – on premises, off premises or in the cloud.

"You have to go through all these data privacy regulations and procedures to get all the approvals," Abrashi said.

AI software vendors need to be flexible about where they install their AI platforms to ensure financial institutions can avoid data privacy issues, Abrashi continued.

"Especially now with all the regulations that are becoming stricter and stricter in terms of what type of data you can share," she said. "Everything now is PII [personally identifiable information]. Not only the first name and the last name, but they consider everything PII."

In the reinsurance industry, AI tools have the potential to solve many problems, but integrating them is the problem, said Dean Marcus, an actuary at Guy Carpenter & Company, a reinsurance company based in New York.

"Partially because of data challenges … but also just because of calibrating the models and ensuring that they're doing what you want them to do in a timely way," Marcus said.

Despite all of these challenges, financial institutions have little choice but to use AI tools to stay competitive, said Michelle Wang, a senior lead management officer at Wells Fargo.

Wang works in the risk management department at Wells Fargo, which uses AI technologies for things like data analytics, she said.

"As newer adopters, there are always challenges to understand how to use the tools that are available to us," she said. "To be able to stay competitive in the game, you have no choice but to develop your AI lab space within your organization."

Author: Esther Ajao

Source: TechTarget