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Banks and insurance companies have a large number of historical consumer data, so they can use these entries to train machine learning models.
Alternatively, they can leverage datasets generated by large telecom or utility companies. The bank aims to increase credit access for customers with thin credit history in Latin America. Destacame accesses bill payment information from utility companies via open APIs. Using bill payment behavior, Destacame produces a credit score for a customer and sends the result to the bank.
Algorithmic trading In algorithmic trading, machine learning helps to make better trading decisions. A mathematical model monitors the news and trade results in real-time and detects patterns that can force stock prices to go up or down. It can then act proactively to sell, hold, or download stocks according to its predictions.
Machine learning algorithms can analyze thousands of data sources simultaneously, something that human traders cannot possibly achieve. Machine learning algorithms help human traders squeeze a slim advantage over the market average. And, given the vast volumes of trading operations, that small advantage often translates into significant profits. Robo-advisory Robo-advisors are now commonplace in the financial domain.
Currently, there are two major applications of machine learning in the advisory domain. Users enter their present financial assets and goals, say, saving a million dollars by the age of A robo-advisor then allocates the current assets across investment opportunities based on the risk preferences and the desired goals.
Recommendation of financial products. Many online insurance services use robo-advisors to recommend personalized insurance plans to a particular user.
Customers choose robo-advisors over personal financial advisors due to lower fees, as well as personalized and calibrated recommendations. In spite of all the advantages of AI and machine learning, even companies with deep pockets often have a hard time extracting the real value from this technology. Financial services incumbents want to exploit the unique opportunities of machine learning but, realistically, they have a vague idea of how data science works, and how to use it.
Time and again, they encounter similar challenges like the lack of business KPIs. This, in turn, results in unrealistic estimates and drains budgets. It is not enough to have a suitable software infrastructure in place although that would be a good start. It takes a clear vision, solid technical talent, and determination to deliver a valuable machine learning development project.
As soon as you have a good understanding of how this technology will help to achieve business objectives, proceed with idea validation. This is a task for data scientists. They investigate the idea and help you formulate viable KPIs and make realistic estimates. Note that you need to have all the data collected at this point.
Otherwise, you would need a data engineer to collect and clean up this data. Depending on a particular use case and business conditions, financial companies can follow different paths to adopt machine learning. Forgo machine learning and focus on big data engineering instead Often, financial companies start their machine learning projects only to realize they just need proper data engineering.
Most companies that aim for machine learning in fact need to focus on solid data engineering, applying statistics to the aggregated data, and visualization of that data. Merely applying statistical models to processed and well-structured data would be enough for a bank to isolate various bottlenecks and inefficiencies in its operations. What are the examples of such bottlenecks? That could be queues at a specific branch, repetitive tasks that can be eliminated, inefficient HR activities, flaws of the mobile banking app, and so on.
Before applying any algorithms, you need to have the data appropriately structured and cleaned up. Only then, you can further turn that data into insights. Use third-party machine-learning solutions Even if your company decides to utilize machine learning in its upcoming project, you do not necessarily need to develop new algorithms and models.
Most machine learning projects deal with issues that have already been addressed. These out-of-the-box solutions are already trained to solve various business tasks. That software applies to various domains, and it is only logical to check if they fit to your business case. A machine learning engineer can implement the system focusing on your specific data and business domain. The specialist needs to extract the data from different sources, transform it to fit for this particular system, receive the results, and visualize the findings.
The trade-offs are lack of control over the third-party system and limited solution flexibility. Ihar Rubanau , a senior data scientist at N-iX comments: A universal machine learning algorithm does not exist, yet. Data scientists need to adjust and fine-tune algorithms before applying them to different business cases across different domains.
So if an existing solution from Google solves a specific task in your particular domain, you should probably use it. If not, aim for custom development and integration Innovation and integration Developing a machine learning solution from scratch is one of the riskiest, most costly and time-consuming options. Still, this may be the only way to apply ML technology to some business cases.
Machine learning research and development targets a unique need in a particular niche, and it calls for an in-depth investigation. If there are no ready-to-use solutions that were developed to solve those specific problems, third-party machine learning software is likely to produce inaccurate results. Still, you will probably need to rely heavily on the open source machine learning libraries from Google and the likes.
Current machine learning projects are mostly about applying existing state-of-the-art libraries to a particular domain and use case. Wer es hardwarenah mag, kann auf cuDNN setzen. Der Einsatz von Machine Learning wirft auch juristische und ethische Fragen: Was ist machbar und was nur Hype?
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