What should your budget be for your small business?

| May 5, 2016

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This infographic is critical because the subject of budgets and marketing plans isn’t talked about enough for small businesses. Small businesses have a high failure rate, but with a little bit more critical thinking and planning they can more intelligently allocate their limited yearly budget towards marketing that works for them and brings in new customers to grow the business.

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OTHER ARTICLES

AI in Fintech: a Wellspring of Opportunities

Article | March 31, 2020

Fintech is considered one of the fastest growing industries, which is hardly surprising. Anyone who’s dealt with a bank recently would be overjoyed to discover a more customer-friendly financial service, with transparent fees and superior customer care. Transferring money through services like Venmo is a lot more convenient than any of the options provided by your local bank. Likewise, shopping for a loan using services like LendingTree is incredibly easy when compared to actually going to your bank, waiting, then dealing with the paperwork.

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5 Ways AI can Transform Fintech Industry

Article | February 25, 2020

Fintech is one of the fastest-growing industries, owing to the rising penetration of internet users. There is a paradigm shift to mobile devices for performing financial transactions and related actions. Behind the booming fintech market, there are several technologies that are contributing to making the system fast, secure, and scalable. One such technology is Artificial Intelligence (AI). The AI in the fintech market is estimated to reach USD 35.40 billion by 2025 ( Mordor Intelligence).

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COVID-19 and its Impact on the Banking Industry

Article | March 26, 2020

European central banks have little ammunition with which to deal with the COVID-19 pandemic. Governments will soon be forced to balance the health of their citizens with economic stability and their own debt. After more than a decade of unprecedented economic growth, the world is facing another global economic recession. Since the 2008 crisis, governments have had time to accumulate enough wealth to prepare for the next crisis.

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Optimizing Risk Management With Machine Learning In Banking

Article | May 26, 2021

The enormous amounts of data accessible to banks and their high demand for forecasting make the financial industry a perfect area for machine learning (ML) to shine. In this article, we explore the current applications of machine learning in banking when it comes to risk management, define its challenges and provide a future outlook. Credit Risk Management For the past few decades, banks have mostly used logistic and probit regression models for credit risk assessments and internal risk management. However, all conventional models inherit the same flaw — they predict outputs based only on linear relationships between input variables. This limitation was exposed in the catastrophic 2008 housing market crash. Although the crisis’s negative consequences have been multiplied by uncontrolled sales of credit default swaps and other complex financial instruments, the fundamental reason for failure was in the inaccurate credit risk model. In the aftermath, with the intent to force financial institutions to provide more detailed reports, The Federal Reserve’s CCAR now requires banks to account for more than 2,000 economic attributes. Consequentially, this also led to other regulating authorities introducing new standards that improve supervisory data quality and reporting. At the same time, with the proliferation of banking apps, social media, and digital communication overall, financial institutions now collect lavish amounts of unstructured data. If gathered and processed correctly, these new datasets can help gauge critical insights for a wide range of banking operations. This is where machine learning comes into play. More advanced non-linear approaches to credit risk modeling including neural networks enable banks to make predictions with a previously unseen level of accuracy and granularity. Challenges The utter superiority of machine learning over traditional credit risk modeling approaches comes at the cost of the prevailing ‘black box’ problem. While we can decide to trust ML algorithms based on statistical evidence of their feasibility, current regulatory constraints won’t allow it to happen. However, machine learning can still be used to a great extent while being regulation-compliant. Even simple linear machine learning approaches still yield more accurate results than conventional ones. Many banks also use unsupervised machine learning methods to explore data, while using traditional classification and regression models to make predictions. Fraud Management and Surveillance Nowadays, the majority of banks’ fraud detection systems use rule-based approaches. This causes banks to deal with a significant number of false positives, forcing them to spend inordinate amounts of resources to distinguish meaningless behavioral deviations from real threats. The ability of machine learning to capture subtle trends and uncover non-linear relationships allows banks to get a complete picture of a client’s activity and significantly lower the probability of false positives. For example, by integrating ML into its fraud detection model, Danske Banks managed to reduce false positives by 60%. Challenges Similar to many other AI-based solutions in the financial space, the biggest adoption hurdles concern regulations and the unexplainability of AI systems. For example, depending on the jurisdiction, banks are often unable to provide developers with sensitive information related to past breaches. Next, the outputs of unsupervised monitoring systems sometimes can’t be explained, which makes them non-compliant. However, financial institutions have found a way to at least partly leverage the power of ML for fraud management. A fraud prevention system’s alerts will still be triggered by rule-based models, but the integration of an ML algorithm on top of them can allow adjusting surveillance methods to a person’s behavior fluctuations. Such ML models are typically less complex and explainable, which makes them applicable in a regulatory context.

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