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Bots, algorithms, and the future of the finance function

23/1/2018

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Automation and artificial intelligence are poised to reshape the finance function. Knowing what to automate and managing the disruption can lead to a new era of productivity and performance.
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For years, a global pharmaceutical company had outsourced its procure-to-pay finance activities, such as processing invoices and paying suppliers. Savings from low-cost labor and improved processes had yielded savings, but managers were eager to explore whether automation could unlock new opportunities. After assessing for themselves how much work could be automatable, estimating the value at stake, and calculating the investment required, they challenged the company’s offshore business-process outsourcer (BPO) to show that it could compete with an automated model. In the end, the pharmaco managers decided not to bring the outsourced elements home to automate. But they did renegotiate the company’s BPO contract, saving 40 percent or more over the next three years.
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Offshoring, outsourcing, and centralization have been the bread and butter of improving the finance function’s productivity for decades. As the pharmaco’s experience shows, tech-savvy CFOs are now considering automation to propel a new wave of efficiency and performance. 

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Behind the hype: Machine learning in investment management

15/8/2017

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Big data: Costly, but is it useful?Although big data is usually directly associated with machine learning, there is still a debate whether new data sources, such as web crawling through news or social media, credit card data, geolocation data, and so on, is helpful in the investment process. Some specific examples of trading strategies based on such data include using Twitter sentiment to make bets on the equity market as a whole or individual stocks in particular or using geolocation data to estimate retail activity relevant to individual stocks (e.g., footfall at retail stores).
The Barclays report states that 54% of surveyed investment managers use alternative data, such as web crawling social media data, satellite data, or credit card data. This finding suggests it is less prevalent than tick data (100% usage) or fundamental data (62% usage), such as balance sheet or income statement data, but more prevalent than economic data (38% usage), such as employment or inflation figures, or sell-side data (31% usage), such as analyst reports or broker recommendations.
Despite the widespread use of alternative data, 80% of surveyed investment managers in the Barclays report said that their biggest challenge was in assessing the usefulness of the data.
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THE IMPACT OF BIG DATA ON BANKING AND FINANCIAL SYSTEMS

25/7/2017

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Today, most banking, financial services, and insurance (BFSI) organizations are working hard to adopt a fully data-driven approach to grow their businesses and enhance the services they provide to customers. Like most other industries, analytics will be a critical game changer for those in the financial sector.
Though many BFSI organizations are beginning to disrupt their analytics landscapes by gathering immense volumes of data assets, these companies are at varying levels of Big Data maturity. In many cases, these initial data projects lead business stakeholders to a very simple question: “How can this data help us solve our business problems?”
As customer volume increases, it dramatically affects the level of services offered by the organization. Existing data analytics practices have simplified the process of monitoring and evaluation of banks and other financial services organizations, including vast amounts of client data such as personal and security information. But with the help of Big Data, banks can now use this information to continually track client behavior in real time, providing the exact type of resources needed at any given moment. This real-time evaluation will in turn boost overall performance and profitability, thus thrusting the organization further into the growth cycle.
Identifying more areas where Big Data resources can be utilized most efficiently involves the alignment of business cases and technological capabilities, which reveals opportunities for improved business processes. There are three primary areas where banks and other financial organizations can attain benefits from advanced analytics: the customer experience, operation optimization, and employee engagement.
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Predictive analytics: Its role in treasury and finance

2/11/2016

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Predicting the future may not be that difficult with the help of ‘big data’.  Sophisticated system design now enables financiers to capture more and more data and it can be put to a myriad of beneficial end uses. Predictive models provide a framework in which to design a window to look through, while ‘big data’ can provide the tools needed to build the window. This is predictive analytics and this article will examine the area and its possible end uses.

It is always advisable to start with a definition and an examination of its scope of operation, so what are the key tenets of predictive analytics? There are many different interpretations but my favourite is to essentially describe it as statistical modelling using historical data to look for patterns in data.  The patterns are then used to identify trends, and the trends are most commonly used to predict outcomes...

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  • What Leaders Should Know
    • What Boards need to know
    • Marketing, Sales & Customer Service
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    • CIOs, IT Directors, CTOs
    • How to get started
    • Executive guide to analytics trends
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      • Use cases - Retail, eCommerce, CPG
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    • Service offerings
    • Training & Education >
      • Executive Workshop agenda
      • In-Company Workshops
      • Webinar - Is your data a cost centre or a key revenue driver?
      • View / download previous workshops
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      • R4 Technologies
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