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TECHNOLOGY

Transforming Financial Services with Artificial Intelligence

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Transforming Financial Services with Artificial Intelligence


Artificial intelligence (AI) is the tech to watch.

IDC forecasts that spending on AI technologies will grow to $97.9 billion in 2023—more than two and a half times the spending level of 2019. 

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Where is AI’s growth coming from? 

On my show CXO Spice Talk, I chatted with Kevin Levitt, Global Business Development Lead – Financial Services from NVIDIA. He says AI’s explosive growth is happening in consumer finance and capital markets. 

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Banks, insurance companies, payment firms and others are investing in AI Centers of Excellence (CoE). His point: if you want to compete for best-in-class customer service, embrace customer-centricity. Beyond personalization, AI is impacting underwriting, fraud detection and every critical applicationAI CoEs are the source of expertise and infrastructure necessary to bring enterprise AI strategies across financial services to fruition.

AI is also being used for investments in algorithmic trading platforms powered by accelerated computing infrastructure to uncover Alpha across thousands (kind of mind boggling, right?) of real-time data sources to either execute trades algorithmically or deliver signal to the human trader. 

Just a month ago, the Chicago Board of Trade opened two floors for trading. The crossroad of #humanity and #tech further unfolds as AI fills in the gap on uncovering insight from geospatial data, audio files (i.e. earnings calls, CEO presentations/interviews, etc.).

Again, we see being in the winner’s spot driving AI to frame out market leading high performance computing environments to tackle risk management, predict credit risk, and more within banks, insurance companies and other industries to maximize investment returns and protect the firm from systemic risks.

NVIDIA’s State of AI in Financial Services 2022 Trends Report explores how AI is transforming the industry in massive ways. It’s easy to think that customer relations is the primary use for AI in industries like retail banking, investment banking, asset management, insurance and fintech. However, NVIDIA’s report shows the AI-driven innovation is much more expansive.

The NVIDIA report reveals that “some 91% of financial services companies are driving critical business outcomes with investments in AI. First and foremost, 43% of respondents state that AI is yielding more accurate models. Along with model accuracy comes a host of other benefits.” Enterprise AI strategies and infrastructure is impacting fraud detection, algorithmic trading, recommender systems whereby customers are given next step actions and compliance.

When AI understands the nuances of language, we will have taken the next big leap. Intuitive, empathetic AI is upon us. Innovators like NVIDIA play a major role in making it happen.  

Dive deeper into the future of AI. Watch the latest CXO Spice Talk episode featuring Kevin Levitt from @NVIDIA AI. Here is the link to explore more:

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Learn more about NVIDIA’s solutions for the financial services industry.



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TECHNOLOGY

The Role of Big Data Analytics in Accounting

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The Role of Big Data Analytics in Accounting


Companies generate enormous amounts of data that need to be processed to produce readable insights and outcomes.

Big data analytics in accounting is a game-changer as it’s improving risk identification and real-time access to data and reporting.

More firms are increasingly adopting newer technologies to make them more efficient. This includes blockchain, artificial intelligence, machine learning, robotic process automation, data analytics, etc. The use of traditional accounting has disrupted the world of accounting, but with the onset of big data analytics, it has gone leaps and bounds, tapping into the untapped potential of any business.

Use Cases of Big Data Analytics in Accounting

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Businesses accumulate tremendous amounts of data that could go into petabytes and zettabytes. The accounting function in any organization records all types of financial and non-financial transactions, collects them and analyzes them using predictive models to find actionable insights. Data analytics is all about making sense of the data received and thus, it takes away the hassle of traditional accounting. Let’s dive into why you would need to transition your business from using conventional to big data analytics.

1. Real-time Reporting

One of the biggest USPs of using big data analytics in accounting is its real-time reporting functionality. Most of the analytical tools available today are cloud-based, making real-time insights and reporting more accessible than ever. As big data deals with a trove of data, it crunches historical data in terabytes and even petabytes to find actionable insights.

2. Real-time Access

Another characteristic of using data analytics in accounting is real-time access. As it is cloud-based, it has the upper hand in timers of data visibility across different functions in an organization. It can be accessed concurrently, and different users can have different privileges for access.

Apart from that, the data syncs so that the changes made in one node are easily accessible on other nodes. This improved access to information in real-time with transparency makes decision-making easier.

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3. Risk Identification and Mitigation

Certain risk factors can prevent a business from outperforming the revenue it hit last quarter or against the rival. Big data can help find risks associated with financial services, such as the supply chain, fraudulent transactions or activities, liquidity, data breach, etc. Businesses can use all the data and add it to various algorithms to anticipate or predict possible outcomes or track fraudulent activities in the books. As accountants can now find errors and risks sooner, the chances of propagating from the point of no return diminish.

4. Data Visualization

Making sense of voluminous data is impossible without using tools such as Tableau. It is a heavily used data visualization tool for big data as it helps find the flow, pattern and irregularities in the dataset. Analyzing the visualized data can assist in making business decisions and strategies needed to adhere to in the future.

Conclusion

Big data analytics in accounting can be a significant driving force toward many use cases. It includes predicting sales performance on food, travel, hospitality and others across different data sources, such as Booking.com, Yelp, etc. It can reduce downtime and operational costs thanks to monitoring IoT sensor data.

Companies can use data analytics in accounting to zero fraudulent activities. Optimizing labor and staff requirements is another chunk of issue that can be curbed using big data based on prediction analysis.

Organizations worldwide are leveraging the power of big data analytics in accounting over the traditional approach. It is because of the many benefits that it brings to the table, including real-time data access and reporting, data visualization, data audits, and others.



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