Article | February 26, 2020
Over the past several years, financial technology -- or fintech, for short -- started a revolution of sorts in what some consider a stodgy industry. Online-only banks with no branches, digital payment systems, and person-to-person (P2P) payment apps are just a few of the ways that technology is changing the way consumers handled their money. That hasn't gone unnoticed by some of the biggest names in the financial services industry, and rather than reinvent the wheel, some are spending hefty sums to acquire the talent and technology that sought to disrupt them in the first place.
Article | February 26, 2020
Going far away beyond conventional attack detection, advanced machine learning operations assist organizations to stay one step ahead of financial fraudsters.
We hear tons of stories about account takeovers and hacking also. How can financial institutions detect and mitigate these attacks?
The world of fraud prevention in banking institutions has always been supported by rules. Bankers and their engineers were uniting rules engines on the banking data system to stop or identify common fraud patterns. For quite a while, this was sufficient. But today we are experiencing a change of society, a digital and technological revolution. Following the primary iPhone, and therefore the later mobile internet explosion, people are interconnected all the time, everywhere and for all quite useful. In this digital age, the digitization of means and behaviors forces corporations to revise their business model. As a result, banking institutions are going massively online and digital-first. Both the bank users and customers have unfolded their behaviors with the brand-new means offered by the digital era.
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With the shift towards universal digitalization, perpetrators are finding new weak spots in financial digital applications. Ironically, the technology works both ways: it accommodates firms to supply more reliable customer experience and optimize operations and, at an equivalent time, aids cybercriminals in performing numerous sophisticated unlawful schemes.
According to the Association of Certified Fraud Examiners (ACFE), 30pc of fraud occurrences happened in small businesses, and 60pc of small-business fraud victims did not retrieve any of their losses.
According to Statista, in 2017, the global FDP (fraud detection and prevention) market was calculated to be worth $16.6 billion.
According to McKinsey, worldwide losses from card fraud could be close to $44 billion by 2025.
Financial crimes do not limit crimes like credit card fraud, tax dodging, and elder abuse. In fact, it includes much broader offenses – such as Identity theft, human trafficking, phishing, pharming, drug trafficking, money laundering, and terrorist financing that can have enduring impacts on society.
Fighting financial fraud is difficult because fraudsters frequently change and adapt. The moment you figure out how to identify and prevent one scam, a unique one emerges to take its spot. Identifying, eliminating, and blocking these threats are sensitive points for e-commerce and banking industries. Sincerely, the best technology for combating fraud is one that can evolve and adapt as instantly as the fraudster’s tactics. That’s what makes machine learning (ML) systems ideal to fight fraud and financial crime.
The big problem is that companies think they need to establish rules, policies, and procedures to prevent fraud. But today’s criminals are much more sophisticated and are able to circumvent these business rules. Businesses need to take a more dynamic approach that includes business rules as well as machine learning and AI to learn from evolving criminal behavior and deliver a more sophisticated and effective approach to dealing with financial crimes.
Andrew Simpson, Chief Operating Officer of CaseWare Analytics.
Why use machine learning to combat financial fraud?
Machine Learning knocks down the conventional ways of detecting fraud. It’s quicker, works with extensive amounts of data, and doesn’t rely on human resources. When designed optimally, it absorbs, adapts, and uncovers emerging patterns without the over-adaptation resulting in too many false positives. It’s time for ML to conclusively take center stage in assisting firms to recognize and counter fraud as fast as it’s performed.
How Machine Learning Helps in Fighting Fraud and Financial Crime?
Machine learning can learn normal behavior from training data and recognize abnormal behaviors that indicates money laundering, like, when money is transferred between suspicious geographies, active movement of funds between different accounts, or invoicing number sequences have been falsified. Machine learning is continually learning, and so they can recognize when the pattern of laundering change and adjust rapidly.
Analyzing Huge Amounts of Transaction Data
One of the most powerful features of machine learning algorithms is that they can analyze huge numbers of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. Its algorithms serve 24/7 and process an immense amount of information with the flip of a switch. This risky analytics method recognizes complex patterns that are challenging for analysts to identify; this means banks and financial organizations are far more operationally proficient while detecting more fraud.
The algorithms take various factors into account, including; customer’s location, the device used, and other circumstantial data points to form a detailed picture of every transaction. This strategy improves real-time decisions and protects customers against fraud, all without affecting the user experience.
Thanks to extensive technological development, organizations will frequently rely on machine learning algorithms to determine which transactions are suspicious.
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Supervised and Unsupervised Learning for Detecting Complex Patterns
Machines can be programmed to self-learn in an unsupervised model with ML so that transactions that do not conform to a set pattern are recognized and hence can be actioned upon in right period.
Machine Learning automatizes the extraction of aware and unaware patterns from data. Once it identifies those patterns, it can employ what it learns to new and unseen data. The machine learns and modifies as new outcomes and new patterns are introduced to it via a feedback loop.
In fraud detection, supervised machine learning algorithms can self-learn from targets within the data. While training a supervised model, it's important to present to it both fraudulent and non-fraudulent records that have been labeled as such.
Unsupervised Machine Learning is different. It reveals potentially unusual risks you might not watch for because it works without a target. Instead, it looks for irregularities in the data.
Machine Learning in Fraud Detection
The fraud detection method employing machine learning starts with gathering and segmenting the data. Alongside this, the machine learning model receives training sets that train it to predict the possibility of fraud. Conclusively, it creates a fraud detection model:
Input data- The first step is data input, which differs in Machine learning and humans. Humans strive to comprehend massive amounts of data, such a task is a five-finger play for ML. The more data an ML model eats, the better it can learn and polish its fraud detection abilities.
Extract Features- Extracted features defining good customer behavior and deceitful behavior are added. These features normally include the customer’s location, identity, orders, network, and preferred payment method. Based on the complexity of the fraud detection system, the list of examined features can vary.
Train Algorithm- Further in this process, a training algorithm is launched. In short, this algorithm is a collection of rules that a machine learning model has to pursue when deciding whether an operation is genuine or fraudulent. The more data a business can supply for a training set, the more reliable the ML model will be.
Create Model- After the training is over, an organization receives a fraud detection model acceptable for their business. This model can detect fraud in no time with great accuracy. To be efficient in credit card fraud detection, a machine learning model needs to be continually improved and updated. Eventually, fraudsters will turn up with new bamboozle to game the system unless you keep it updated.
Employing advanced fraud protection and detection systems electrified by ML, multiple industries can keep their finances secured.
Capgemini alleges their ML fraud detection system can lessen fraud investigation time by 70% while boosting accuracy by 90%.
Another ML fraud prevention solution provider, Feedzai, alleges that a well-trained machine learning solution can recognize and prevent 95% of all fraud while reducing the amount of human labor needed during the investigation stage.
Reduction of False Positives
With the level of complicatedness in today’s financial infrastructures, the term ‘false positive’ has become nearly correlated with the industry’s efforts to fight fraud. One of the banking’s most significant challenges is to minimize the number of false positives being generated, thereby saving time, money, and bypassing needlessly frustrating customers.
AI and machine learning play a significant role in this area. Because they are proficient in examining a much more comprehensive set of data points, connections between entities and fraud patterns – including fraud scenarios not yet known to fraud analysts – the predominance of false positives can be extremely reduced.
Bringing it all Together
Multinationals like Airbnb, Yelp, and Jet.com are already employing AI solutions to get insights from big data and counter issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning entertains all the messy work of data analysis and predictive analytics and empowers companies to grow and develop secure from financial fraud and crime.
As mentioned, machine learning can be very convenient when it comes to fighting cybercrimes. ML prevents critical attacks on users’ and companies’ finances. It’s a quick, up-to-date, and cost-effective method to shield customers and the company’s data.
Article | February 26, 2020
The financial crisis of 2008 levelled the legacy banking industry quickly and decisively in North America. And rebuilding has been a long process — culturally, operationally and technologically. With the market once again in a state of volatility in part due to the spread of COVID-19, we’re facing the question once again: what does it take for financial institutions to weather the storm?
Article | February 26, 2020
Digital transformation had a huge impact on every sector, and this includes finance. Digital disruption in finance can be defined as an event where new technologies replace traditional methods for financial transactions. This article will discuss how technology for finance has changed over time, why it's vital to stay current with digital trends, and what you need to do to make your company ready for the future!
Make plans for the coming age of Digital Transformation
The finance team has traditionally used tools like spreadsheets, reports, and presentations to managing its processes. However, digital disruption in finance is creating new challenges for the finance department because of these changes - they need to learn how to use technology effectively or risk getting left behind! Here are some examples demonstrating why digital transformation of your company's financial processes can be beneficial.
Digital transformation gives the finance team access to better systems to help them do their jobs more efficiently and effectively.
Digital transformation gives the finance team access to better tools that will allow them to be more agile and deliver new services for their clients.
Digital transformation saves money for the company because new technology is cheaper than old technology like spreadsheets and presentations.
As such, if you want your company ready for the future, make sure your finance department is aware of digital trends and knows how it can integrate new technology into its workflows.
Prepare for a transaction revolution as automation and blockchain infiltrate further into the financial process:
While financial institutions have been working on transformation plans for many years, the recent cryptocurrency and blockchain revolution indicates that things are moving faster than ever before. As a result, banks are starting to understand that they need to be open to new technologies and ways of working to stay relevant in their industries and attract new clients. Financial institutions need to brainstorm new ideas and innovative ways of working that will allow them to be relevant in today's market while at the same time applying technology in ways that facilitate faster and safer processes.
Financial Institution's role in Digital Transformation:
Now that most finance processes are automated, the finance industry will provide more business insights and services. Of course, success is not a certainty, but digital marketing for financial services can get more focused and accurate.
In essence, FinTech technology will improve financial management and help production. It can do this by rethinking procedures, breaking formats (finance is a chaotic environment), streamlining reporting, and endorsing transactions with a better data set. As a result, businesses may find themselves better placed to make long-term decisions and do not require immediate cash flows. The key question is whether financial technology can deliver on these promises in a way that provides real benefits for customers and shareholders while being price-insensitive enough to be affordable for all.
The way for financial teams to be agile:
Although digital transformation is a competency within finance, most bankers still focus on software development and hopping from platform to platform. One of the biggest problems in overcoming this difficulty is the lack of a common language. An effective digital transformation strategy requires the sharing of data - including between departments. Sharing information enables agility because it allows each team to understand their strengths and weaknesses more clearly. It also enables cross-functional teams that can reach out to business partners outside their core business functions when necessary or to solve problems outside their domain of specialty. The transformation can also help in creating compelling promotions and creating persuasive advertisements for your financial services.
"As workplaces start to open, a hybrid model—seems to be a new norm that provides flexibility for people to operate both from their homes and offices, as we emerge out of the pandemic period."
Vice President (Model Validation) at Citi
A more adaptable future in digital transformation for FinTech
Several banks and financial institutions are making it a point to associate themselves with technological innovation. Recent data shows that nearly half of financial institutions worldwide have made some sort of digital transformation in the past five years – from transaction processing to customer relationship management. Increasingly, these institutions are looking to the future and thinking about using technology to transform how they do business. Digital transformation of finance is just one of the many buzzwords we're hearing from financial institutions right now. Financial institutions need to stay connected and relevant in an increasingly competitive marketplace by designing financial products and services that meet their changing demands.
Cloud computing for a more agile future
What's more, the shift means businesses can scale faster using the cloud--perhaps even more effectively--than before. The momentum behind the online collaboration, instant messaging, and Web browsing has only accelerated in the past few years -- threatening to upend the very foundations on which many large companies have built their business models. This shift means banks will need to find new ways to stay competitive and fast. Tech giants such as Amazon, Microsoft, and Google make significant inroads with cloud technology into innovative services and products into the bank space. We'll see if they also can help shake up the way work gets done -- either here or in offices around the world, as migrating infrastructure to the cloud enhances access, flexibility, and scalability for both FinTechs and banking giants.
The current financial crisis has led to a re-examination of traditional finance models and ways of working. One area that has gained particular attention is the digital transformation of finance teams. The potential impact of digital transformation on finance is intense. If well-timed, it may help finance organizations attract and retain talented employees while reducing operating costs and enhancing returns on investment in core operations.
What are the four main areas of digital transformation?
Digital transformation is a broad term that generally refers to an increase in efficiency across many business functions using technologies such as software applications, data analysis techniques, networks, and infrastructure. To achieve digital transformation, the organization needs to rethink many core processes while integrating new technologies. In addition, there are challenges associated with changing from an existing model and overcoming internal resistance.
What is the future of finance?
The future of finance is in changing the way companies raise money for new ventures and how financiers themselves manage their portfolios. Fundamental changes include using technology that helps investors access data and choose more suitable investments, better deals, and structures for companies that now seek to raise money from multiple sources rather than just raising an individual round.
What are the top technologies for finance?
Technologies used in the financial services sector have become so integrated that it is difficult to understand their impact on a business or industry. Nevertheless, here are some of the emerging technologies that are in use right now:
Hybrid Cloud Servers
Robotic Process Automation
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"text": "Digital transformation is a broad term that generally refers to an increase in efficiency across many business functions using technologies such as software applications, data analysis techniques, networks, and infrastructure. To achieve digital transformation, the organization needs to rethink many core processes while integrating new technologies. In addition, there are challenges associated with changing from an existing model and overcoming internal resistance"
"name": "What is the future of finance?",
"text": "The future of finance is in changing the way companies raise money for new ventures and how financiers themselves manage their portfolios. Fundamental changes include using technology that helps investors access data and choose more suitable investments, better deals, and structures for companies that now seek to raise money from multiple sources rather than just raising an individual round."
"name": "What are the top technologies for finance?",
"text": "Technologies used in the financial services sector have become so integrated that it is difficult to understand their impact on a business or industry. Nevertheless, here are some of the emerging technologies that are in use right now:
Hybrid Cloud Servers
Robotic Process Automation