Last year I proposed that Poland should take a broader approach to the issue of regulatory sandboxes, not merely copying solutions adopted in other countries. Now we see the first steps towards creation of multi-sectoral (not exclusively financial) regulatory sandboxes.
The trend started by the Financial Conduct Authority in the UK of creating regulatory sandboxes for the financial sector has spread around the world, including Poland. Although many voices from major jurisdictions, such as the United States and Germany, are skeptical, this solution undoubtedly has its advantages. Market participants usually rate this concept very highly, even if in reality the sandbox does not deliver immediate regulatory benefits (for example, it does not enable limited operation of regulated activity without a licence, which would be difficult in EU member states due to the harmonised regulatory regime).
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An interview with Karol Kłaczyński, Agnieszka Poteralska, Artur Tanona and Maciej Zalewski, members of the team that won first place in the Polish phase of the Global Legal Hackathon.
You won the Polish phase of the Global Legal Hackathon with a solution that you yourselves describe as “a plug-in to Word,” but which has the chance to truly expedite the work of lawyers. What is your concept all about?
Karol Kłaczyński: InteliLex provides quick access to the document database created at the given organisation. In our discussions with lawyers this problem often comes up. The knowledge exists, it has been developed, but searching for it is time-consuming and inefficient. InteliLex helps improve the efficiency of the search.
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Regulatory sandboxes usually focus on financial regulations. However, these are not the only obstacle holding back innovative fintech start-ups. More extensive sandbox solutions that also cover data protection issues could make Poland a pioneer on the attractive global market supporting fintech.
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Many startups offer their clients big data analysis services based on machine-learning algorithms. The results of such analyses can be of interest to any companies profiling their products or marketing campaigns. But for the analysis to be reliable, it takes data—the more the better. Algorithms using machine learning must have something to learn from. The accuracy of the forecasts subsequently developed for business aims will depend on the scope of the training data fed to them. If the algorithm is limited from the start to analysis of an abridged sample of observations, the risk increases that it will incorrectly group data, overlooking important correlations or causal connections—or see them where they don’t exist. Only training the algorithm on large datasets can minimise the risk of shortcomings in diagnosis and prognosis.
Continue reading “Small firms, big data”