4 items tagged "Banking"

  • Big Data Analytics in Banking

    Big Data Analytics in Banking

    Banking institutions need to use big data to remodel customer segmentation into a solution that works better for the industry and its customers. Basic customer segmentation generalizes customer wants and needs without addressing any of their pain points. Big data allows the banking industry to create individualized customer profiles that help decrease the pains and gaps between bankers and their clients. Big data analytics allows banks to examine large sets of data to find patterns in customer behavior and preferences. Some of this data includes social media behavior.

    • Demographic information.
    • Customer spending.
    • Product and service usage — including offers that customers have declined.
    • Impactful life events.
    • Relationships between bank customers.
    • Service preferences and attitudes toward the banking industry as a whole.

    Providing a Personalized Customer Experience with Big Data Analytics

    Banking isn’t known for being an industry that provides tailor-made customer service experiences. Now, with the combination of service history and customer profiles made available by big data analytics, bank culture is changing. 

    Profiling has an invasive ring to it, but it’s really just an online version of what bankers are already doing. Online banking has made it possible for customers to transfer money, deposit checks and pay bills all from their mobile devices. The human interaction that has been traditionally used to analyze customer behavior and create solutions for pain points has gone digital. 

    Banks can increase customer satisfaction and retention due to profiling. Big data analytics allows banks to create a more complete picture of what each of their customers is like, not just a generic view of them. It tracks their actual online banking behaviors and tailors its services to their preferences, like a friendly teller would with the same customer at their local branch. 

    Artificial Intelligence’s Role in Banking

    Nothing will ever beat the customer service you can receive in a conversation with a real human being. But human resources are limited by many physical factors that artificial intelligence (AI) can make up for. Where customer service agents may not be able to respond in a timely manner to customer inquiries depending on demand, AI can step in. 

    Chatbots enable customers to receive immediate answers to their questions. Their AI technology uses customer profile information and behavioral patterns to give personalized responses to inquiries. They can even recognize emotions to respond sensitively depending on the customers’ needs. 

    Another improvement we owe to AI is simplified online banking. Advanced machine learning accurately pulls information from documents uploaded online and on mobile apps. This technology is the reason why people can conveniently deposit checks from their smartphones. 

    Effective Fraud Prevention

    Identity fraud is one of the fastest growing forms of theft. With more than 16 million identity theft cases in 2017, fraud protection is becoming increasingly important in the banking industry. Big data analytics can help banks in securing customer account information.

    Business intelligence (BI) tools are used in banking to evaluate risk and prevent fraud. The big data retrieved from these tools determines interest rates for individuals, finds credit scores and pinpoints fraudulent behavior. Big data that’s analyzed to find market trends can help inform personal and industry-wide financial decisions, such as increasing debt monitoring rates.

    Similarly, using big data for predictive purposes can also help financial institutions avoid financial crises before they happen by collecting information on things like cross-border debt and debt-service ratios.

    The Future of Big Data Analytics

    The banking industry can say goodbye to their outdated system of customer guesswork. Big data analytics have made it possible to monitor the financial health and needs of customers, including small business clients. 

    Banks can now leverage big data analytics to detect fraud and assess risks, personalize banking services and create AI-driven customer resources. Data volume will only continue to increase with time as more people create and use this information. The mass of information will grow, but so will its profitability as more industries adopt big data analytic tools. 

    Big data will continue to aid researchers in discovering market trends and making timely decisions. The internet has changed the way people think and interact, which is why the banking industry must utilize big data to keep up with customer needs. As technology continues to improve at a rapid pace, any business who falls behind may be left there.

    Author: Shannon Flynn

    Source: Open Data Science

  • Blockchain-based banking backend Vault OS from ex-Googler emerges from stealth mode

    vaultos featureDespite holding the vast majority of the world’s wealth (or perhaps because of that), banks aren’t exactly hotbeds of cutting-edge tech, often relying on decades-old systems for everyday tasks. ThoughtMachine, a company led by ex-Google engineer Paul Taylor, is looking to change that with a modern, fully integrated, blockchain-based banking operating system called Vault OS.

    The bombastic press release announcing the system’s emergence from two years of stealth development makes a lot of promises: the company “has solved the greatest challenge in fintech;” Vault OS is “100% future-proof,” “hugely flexible,” and “fixes broken banking forever.”

    Whether Vault OS is able to live up to its own hype is a question that will have to wait (legacy banking systems aren’t replaced overnight) — but it’s hard to deny that the problem is real and the solution, or at least what the company reveals of it, is compelling.


    ThoughtMachine’s Paul Taylor

    The main job of Vault OS is to perform the core function of a bank: essentially, maintaining a huge ledger. That’s something that a blockchain is uniquely suited to doing, of course, a fact that clearly did not escape Taylor, whose previous work led to the speech recognition software used by Google today.

    Each instance of the OS will run its own private blockchain and cryptographic ledger, hosted as a service by ThoughtMachine. Of course, whether banks will be willing to essentially permanently outsource their most fundamental operations is yet another big question.

    The benefits may be worth it: blockchains are secure, scalable, and versatile, and could conceivably replace legacy systems that limit or delay ordinary operations. Transactions would occur in real time, and are safely and centrally stored, allowing for deep data dives by both bankers and consumers. There’s even an API.

    Naturally there are a ton of questions that must be answered, and assurances made, and regulations complied with, before any bank will touch this with a ten-foot pole. I’ve contacted ThoughtMachine with several — will they release code or a whitepaper for inspection? How is data migration handled? What’s the timescale for rollout? — and will update this post if they get back to me.

    Source: Techcrunch

  • How Big Data leaves its mark on the banking industry

    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

  • The big data race reaches the City

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    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.

    The world is awash with information as never before. According to IBM, 90pc of all existing data was created in the past two years. Once the preserve of academics and the geekiest hedge fund managers, the ability to harness huge amounts of noise and turn it into trading signals is now reaching the core of the financial industry.

    Last year was one of the toughest since the financial crisis for asset managers, according to BCG partner Ben Sheridan, yet they have continued to spend on data management in the hope of finding an edge in subdued markets.

    “It’s to bring new data assets to bear on some of the questions that asset managers have always asked, like macroeconomic movements,” he said.

    “Historically, these quantitative data aspects have been the domain of a small sector of hedge funds. Now it’s going to a much more mainstream side of asset managers.”

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    Banks are among the biggest investors in big data

    Even Goldman Sachs has entered the race for data, leading a $15m investment round in Kensho, which stockpiles data around major world events and lets clients apply the lessons it learns to new situations. Say there’s a hurricane striking the Gulf of Mexico: Kensho might have ideas on what this means for US jobs data six months afterwards, and how that affects the S&P stock index.

    Many businesses are using computing firepower to supercharge old techniques. Hedge funds such as Winton Capital already collate obscure data sets such as wheat prices going back nearly 1,000 years, in the hope of finding patterns that will inform the future value of commodities.

    Others are paying companies such as Planet Labs to monitor crops via satellite almost in real time, offering a hint of the yields to come. Spotting traffic jams outside Wal-Marts can help traders looking to bet on the success of Black Friday sales each year – and it’s easier to do this from space than sending analysts to car parks.

    Some funds, including Eagle Alpha, have been feeding transcripts of calls with company executives into a natural language processor – an area of artificial intelligence that the Turing test foresaw – to figure out if they have gained or lost confidence in their business. Trades might have had gut feelings about this before, but now they can get graphs.

    biggest spenders

    There is inevitably a lot of noise among these potential trading signals, which experts are trying to weed out.

    “Most of the breakthroughs in machine-learning aren’t in finance. The signal-to-noise ratio is a problem compared to something like recognising dogs in a photograph,” said Dr Anthony Ledford, chief scientist for the computer-driven hedge fund Man AHL.

    “There is no golden indicator of what’s going to happen tomorrow. What we’re doing is trying to harness a very small edge and doing it over a long period in a large number of markets.”

    The statistics expert said the plunging cost of computer power and data storage, crossed with a “quite extraordinary” proliferation of recorded data, have helped breathe life into concepts like artificial intelligence for big investors.

    “The trading phase at the moment is making better use of the signals we already know about. But the next research stage is, can we use machine learning to identify new features?”

    AHL’s systematic funds comb through 2bn price updates on their busiest days, up from 800m during last year’s peak.

    Developments in disciplines such as engineering and computer science have contributed to the field, according to the former academic based in Oxford, where Man Group this week jointly sponsored a new research professorship in machine learning at the university.

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

    Dr Ledford said the technology has applications in driverless cars, which must learn how to drive in novel conditions, and identifying stars from telescope images. Indeed, he has adapted the methods used in the Zooniverse project, which asked thousands of volunteers to help teach a computer to spot supernovae, to build a new way of spotting useful trends in the City’s daily avalanche of analyst research.

    “The core use is being able to extract patterns from data without specifically telling the algorithms what patterns we are looking for. Previously, you would define the shape of the model and apply it to the data,” he said.

    These technologies are not just been put to work in the financial markets. Several law firms are using natural language processing to carry out some of the drudgery, including poring over repetitive contracts.

    Slaughter & May has recently adopted Luminance, a due diligence programme that is backed by Mike Lynch, former boss of the computing group Autonomy.

    Freshfields has spent a year teaching a customised system known as Kira to understand the nuances of contract terms that often occur in its business.

    Its lawyers have fed the computer documents they are reading, highlighting the parts they think are crucial. Kira can now parse a contract and find the relevant paragraphs between 40pc and 70pc faster than a human lawyer reviewing it by hand.

    “It kicks out strange things sometimes, irrelevancies that lawyers then need to clean up. We’re used to seeing perfect results, so we’ve had to teach people that you can’t just set the machine running and leave it alone,” said Isabel Parker, head of innovations at the firm.

    “I don’t think it will ever be a standalone product. It’s a tool to be used to enhance our productivity, rather than replace individuals.”

    The system is built to learn any Latin script, and Freshfields’ lawyers are now teaching it to work on other languages. “I think our lawyers are becoming more and more used to it as they understand its possibilities,” she added.

    Insurers are also spending heavily on big data fed by new products such as telematics, which track a customer’s driving style in minute detail, to help give a fair price to each customer. “The main driver of this is the customer experience,” said Darren Price, group chief information officer at RSA.

    The insurer is keeping its technology work largely in-house, unlike rival Aviva, which has made much of its partnerships with start-up companies in its “digital garage”. Allianz recently acquired the robo-adviser Moneyfarm, and Axa’s venture fund has invested in a chat-robot named Gasolead.

    EY, the professional services firm, is also investing in analytics tools that can flag red flags for its clients in particular countries or businesses, enabling managers to react before an accounting problem spreads.

    Even the Financial Conduct Authority is getting in on the act. Having given its blessing to the insurance sector’s use of big data, it is also experimenting with a “sandbox”, or a digital safe space where their tech experts and outside start-ups can use real-life data to play with new ideas.

    The advances that catch on throughout the financial world could create a more efficient industry – and with that tends to come job cuts. The Bank of England warned a year ago that as many as 15m UK jobs were at risk from smart machines, with sales staff and accountants especially vulnerable.

    “Financial services are playing catch-up compared to some of the retail-focused businesses. They are having to do so rapidly, partly due to client demand but also because there are new challengers and disruptors in the industry,” said Amanda Foster, head of financial services at the recruiter Russell Reynolds Associates.

    But City firms, for all their cost pressures, are not ready to replace their fund managers with robots, she said. “There’s still the art of making an investment decision, but it’s about using analytics and data to inform those decisions.”

    Source: Telegraph.co.uk, October 8, 2016



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