Financial Services


Sinequa Featured in IDC Technology Spotlight Dedicated to Financial Services Organizations

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With increased regulatory pressures, data silo proliferation and cognitive drain on analysts, AI-powered platforms become a key enabler to extract insights from data. Today, we announced that Sinequa is featured in a new IDC Technology Spotlight report: Financial Services Organizations: Extracting Powerful Insights with AI-Powered Platforms. The report, written by Steven D'Alfonso, research director, IDC Financial Insights, and David Schubmehl, research director, Cognitive/AI Systems, highlights the importance of AI-powered platforms in their ability to extract insights from data as well as the need for financial services organizations (FSOs) to improve their capabilities to derive insights from the data they possess. According to the report, collecting and maintaining increased amounts of data related to their clients and portfolios can provide major opportunities to improve the customer experience and increase revenue while reducing risk. But at the same time, too much data can be a cognitive drain on analysts and knowledge workers.


As fintech faces more automation, clients should still come first

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While much of today's fintech debate focuses on the potential applications of the technology to financial services, just as important are the underlying reasons why the industry is pursuing innovation so aggressively right now. To a large extent, it reflects the realities of the current environment – the need to reduce costs, to achieve regulatory compliance, to protect against new forms of risk and to stay relevant in a fast-moving and uncertain environment. Every fintech discussion should start and end with the same question: how can we use technology to enhance the client experience? To answer this, we need to understand which technologies will have the greatest impact and deliver the most client value today, tomorrow and in the future. Of all the current innovations, robotic process automation (RPA), or bots for short, are the most ubiquitous in financial services today – and they're already improving the client experience, both in our personal and professional lives.


The Future is here- AI and Machine Learning in Financial Services

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What is the difference between AI and Machine learning? AI is the concept of machines performing tasks that are characteristic of human intelligence -- it is the all-encompassing phase that is highlighted in multiple Sci-Fi movies like Terminator, Matrix, etc. The concept of AI is to address things like recognizing objects and sounds, learning, planning and problem solving. Today most of the AI used in a business context is specific to one area, it displays characteristics of the human intelligence in one specific area like sound, image recognition or problem solving. The evolution of AI to replicate multiple aspects of human intelligence is the next stage in its evolution and that is the focus of new emerging AI initiatives across industries.


How data and tech and will drive financial services change in 2019

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How much has the pace of financial services industry innovation accelerated? The last 10 years have upped the stakes to improve client experiences like no other time in history--ushering in enormous omnichannel technology investment. During this time, we also saw robotic process automation emerge, with its power to improve efficiency by managing data entry between legacy systems. Yet the pace of change and scale of innovation over the past decade will pale compared to what lies ahead. Jim Marous, owner of the Digital Banking Report, said on a recent BAI Banking Strategies podcast, "The pace of change will never be this slow again"--a statement you can time not with a calendar, but with a stopwatch.


STAC Floats ML Benchmark for Financial Services Workloads

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"This study was designed to illustrate how STAC Benchmarks for machine learning (ML) can be constructed and used. It is also intended to help data scientists and data engineers know what to expect when using the data science tools and cloud products of this project and how to avoid common pitfalls. The workload is topic modeling of SEC Form 10-K filings using Latent Dirichlet Allocation (LDA), a form of natural language processing (NLP)," according to the report. STAC's foray into ML/DL benchmarking was presented with both caution and ambition: "While we hope these results are informative, it is important to understand what they are not. They are not competitive benchmark results of the sort readers are accustomed to finding in STAC Reports. No vendors contributed to optimization of the SUTs, so we can be fairly certain that they don't represent the best possible results. As soon as the [STAC] Council adopts these or other benchmark specifications for ML, the competitive benchmark numbers will begin to flow."


Collaboration key to leveraging AI and machine learning in financial advice

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Greater collaboration across the Australian financial services industry is required in order to help financial advisers leverage the full potential of artificial intelligence and machine-learning technology, according to education provider Kaplan Professional and regtech pioneer Red Marker. Matt Symons, Red Marker CEO, said there needs to be a mindset change in how AI and machine-learning tools can best assist the industry. "It is important both dealer groups and vendors progress with realistic expectations, particularly around the'pre-work' that needs to be done to ensure financial advice can become an ideal candidate for automated solutions," Symons said. "If the financial services industry wants to increase the likelihood that effective statement of advice (SoA) review solutions emerge at a faster rate, then we need to come together and collaborate... working together is going to be key to developing highly reliable, automated review solutions." The two organisations said that before the industry could leverage AI and machine learning in financial advice, existing pre-conditions needed to be in place, including managing expectations, recognising the limitations in training data, and resolving diverging approaches to SoA construction, automatic programming language, and product comparison logic.


4 Critical Considerations For Implementing AI in the Banking Industry

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From business innovations and media headlines to TV and movies, it seems that artificial intelligence (AI) is virtually everywhere. While still in its early stages across the financial services industry, AI adoption is expected to accelerate over the next few years. And it's expected to save companies big bucks. According to a recent study by Accenture, 77% of banks plan to use AI to automate tasks to a large extent in the next 3 three years. In addition, a recent study by Autonomous Next, indicates the potential cost savings of using AI could total $450 billion across the banking industry by 2030.



The Future of Wealthtech: Artificial Intelligence, Machine Learning, and Big Data Analytics FintekNews

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Wealth management industry is swiftly changing with the emergence of new business models and modern technology. Artificial intelligence (AI), machine learning (ML), and big data are spearheading this dynamic evolution and fostering the growth of wealthtech. More and more players are employing these tools in order to simplify business processes and help clients reach sound financial decisions through personalized advice. Indeed, this practice is linked to incredible benefits: it saves humans a wealth of time and ultimately, improves the investment outcomes. We live in the age of big data and information transparency, where cutting-edge tech tools can be applied to the entirety of the management value chain.


Here is the Payworld case study on how they implemented chatbot on their website.

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Payworld which is India's largest mobile financial services companies. The company provides financial services such as money transfer, recharges, bill payments, insurance point of sale and GST filling etc. Here is the case study about how payworld solved their problem with the help of a chatbot. Being one of India's largest mobile financial services, payworld encounters hundreds of retailer queries on daily basis. Which made them try a solution that would help them reduce on an average 50-60% of queries.