Tag Archives: Artificial intelligence

Fintech for Supply Chain Finance: Streamlining Payments and Working Capital Management

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How fintechs are revolutionizing the supply chain finance landscape.

How fintechs are revolutionizing the supply chain finance landscape.

The supply chain is the global economy’s backbone. It includes all of the activities involved in delivering goods or services from the manufacturer to the end user. Efficient supply chain financing is crucial for firms to maintain smooth operations.

However, supply chain financing can be complicated and costly due to the numerous players involved. This is where fintech enters the picture. This article will look at how fintech is helping to streamline payments and working capital management in supply chain finance.

What Exactly Is Supply Chain Finance?

Supply chain finance refers to a group of financial solutions aimed at optimizing the movement of cash along the supply chain. It consists of a variety of activities, such as invoice factoring, purchase order financing, and inventory finance. These solutions assist organizations in better managing their cash flow by giving access to working capital as needed.

However, supply chain finance can be complicated and costly. The typical technique comprises many middlemen, such as banks, insurance, and factoring firms, each with its own set of fees. This might lead to a lengthy and costly procedure with little transparency or flexibility.

How Fintech Is Helping to Simplify Supply Chain Finance

Fintech is changing the way supply chain finance is done. Fintech companies are streamlining payments and working capital management by embracing digital technology, making it easier and more cost-effective for businesses to manage their supply chains.

Fintech’s Advantages in Supply Chain Finance

There are numerous advantages to employing fintech for supply chain finance. Increased efficiency is one of the primary advantages. Automation and digital technology are being used by fintech companies to streamline the supply chain financing process, decreasing the time and cost associated. This allows organizations to concentrate on their core operations while improving overall efficiency.

Fintech Risks in Supply Chain Finance

While fintech has numerous advantages for supply chain financing, it also has some drawbacks. Cybersecurity is one of the most serious threats. Fintech firms keep sensitive financial data, rendering them vulnerable to hackers. Businesses should choose a trustworthy fintech supplier with strong security procedures in place to safeguard their data.

How Fintech is Revolutionizing Supply Chain Finance with Artificial Intelligence

Supply chain finance has become an essential tool for businesses looking to optimize their cash flow and improve their working capital management. By leveraging the power of technology, fintech companies are now incorporating artificial intelligence (AI) into supply chain finance, revolutionizing how businesses manage their supply chains and providing unprecedented efficiency and transparency.

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How AI Can Solve Supply Chain Financial Management Challenges

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Never has the issue of supply chain management been so immense

In particular, Covid-19 has made these challenges all the more prominent, with unprecedented pressure on the supply chain after lockdowns and varying restrictions imposed by different countries around the world. Businesses within the supply chain must be resilient and adaptable as the combination of changes that are underway, such as increased globalisation, digitalisation, and driver and other skill shortages, have increased the industry’s complexity. While Covid-19 restrictions have eased and many countries are learning to live with the virus, the supply chain crisis isn’t going away. Political unrest has hampered the movement of products and services worldwide, notably to and from China and, more recently, Russia.

Artificial intelligence (AI) has been cited as a solution to some of the problems businesses within the supply chain. Over half (53%) of UK supply chain decision-makers believe AI advances are crucial to managing disruption. On the finance side, technologies such as AI are being used by innovative companies to better understand their capital through data analytics and performance insights so they can meet their goals through effective financial management. However, data and the overarching strategy must be in the right state to effectively utilise AI, analytics, and data science.

Top three financial management data challenges

1. Granular financial management

Calculating important metrics such as cost to serve is vital for any supply chain business. Still, it can be difficult without real-time data visibility across your service, costs, and inventory. Platforms for enterprise resource planning (ERP) and supply chain management (SCM) produce information on point of sale, inventory, manufacturing, warehousing, and transportation. You can optimise your supply chain if you know how to analyse this data, spot patterns, identify trends, and produce insights. By implementing a supply chain data strategy, you can eliminate complex supply chain issues by implementing a plan backed up by accurate financial data.

2. Data integration & data silos

The use of multiple essential applications is standard practice in logistics businesses, with typical applications including financial planning and analysis (FP&A), delivery planning, warehouse management (WMS), and order management. There are various leadership roles responsible for channels, territories, and products, although traditional monthly management accounts are aggregated at a level above these operational roles at the company P&L level.

3. Data sharing across the supply chain

Within the supply chain industry, it’s important to share data with third parties, including partners, suppliers, and customers – quickly, in as near real-time as possible – to make decisions fast.

Data and AI in action

AI can be embedded into your data platform – it enables you to use predictive analytics to get better insights into all levels of the supply chain – an improved understanding of demand fluctuations and their effect throughout the supply chain. AI data models can help deliver competitive advantage, improve financials and help businesses gain control across many areas. Implementing a big data platform is critical to get insights in real-time or daily. With so much data at hand, the platform must be scalable to ensure success.

This requires breaking down data silos, joining data across the organisation, and using modern advanced analytics in a performant, scalable, and cost-effective data platform with data governance in place.

 

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

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Architecting a Machine Learning System for Risk

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Architecting a Machine Learning System for Risk

Online risk mitigation

At Airbnb, we want to build the world’s most trusted community. Guests trust Airbnb to connect them with world-class hosts for unique and memorable travel experiences. Airbnb hosts trust that guests will treat their home with the same care and respect that they would their own. The Airbnb review system helps users find community members who earn this trust through positive interactions with others, and the ecosystem as a whole prospers.

We can mitigate the potential for bad actors to carry out different types of attacks in different ways.

1) Product changes

Many risks can be mitigated through user-facing changes to the product that require additional verification from the user.

2) Anomaly detection

Scripted attacks are often associated with a noticeable increase in some measurable metric over a short period of time.

3) Simple heuristics or a machine learning model based on a number of different variables

Fraudulent actors often exhibit repetitive patterns.

 

Machine Learning Architecture

Different risk vectors can require different architectures. For example, some risk vectors are not time critical, but require computationally intensive techniques to detect. An offline architecture is best suited for this kind of detection. For the purposes of this post, we are focusing on risks requiring realtime or near-realtime action. From a broad perspective, a machine-learning pipeline for these kinds of risk must balance two important goals:

1) The framework must be fast and robust.

That is, we should experience essentially zero downtime and the model scoring framework should provide instant feedback.

2) The framework must be agile.

Since fraud vectors constantly morph, new models and features must be tested and pushed into production quickly.

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KB – Neural Data Mining with Python sources

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KB – Neural Data Mining with Python sources

The aim of this book is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify.

The algorithms of computing contained within the book are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.

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