01Summary: Swedish FSA releases consumer protection report
The Swedish Financial Supervisory Authority (FI) releases a yearly consumer protection report featuring customer security highlights in the Swedish financial industry. As in the previous years, the main risks related to customer security relate to mortgages and loans. The interest payments can potentially threaten the economies of the individuals in case of the economic downturn or the increase in interest rates. Another threat that is amplifying in the context of the digitalising society relates to customer data protection. The FI calls for more advanced security systems that would protect the consumer at all stages of a payment transaction. The improvement of these solutions is especially relevant in the context of increased instances of financial fraud. Finally, FI announces that the protection of the wealth management customers and the enforcement of the MiFID II requirements regarding third-party inducements becomes a vital area of the regulators' future work.Read More
02March 2019 News Update
We've selected the most relevant global news within the fields of automated financial advice, data intelligence and balance sheet risk this March. The rise of the robo-advisors concern the brokers, although many see this development as a helpful complement to the traditional wealth management business, stressing the regulatory burden as well as IT legacy systems' challenges. Meanwhile, Brexit leads to a spike in risk-aversion among the customers of Do-It-Yourself investment platforms. On the balance sheet risk side, the experts stress the importance of timely preparations to IFRS 17, FRTB and the transition from LIBOR. The machine learning tools are put to use in fraud detection in the banking industry context, and the Positive-Incentive ESG-based Loans gain prominence among the banks and corporations.Read More
Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.Download
Machine learning applications have become more prominent in the financial industry in recent years. Our new article series is exploring the benefits and challenges of using self-normalising neural networks (SNNs) for calculating liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study for the Swedish bond market.
In the third and concluding article in the ALM using LMSC series, we focus on analyzing the optimal asset allocations in the context of changing asset classes as well as finding the optimal allocation by maximizing the risk-adjusted net asset value. The estimates based on the LSMC method are then compared to the estimates obtained from the full nested Monte Carlo method.
The second part of the series exploring the use of Least Squares Monte Carlo in Asset and Liability Management is focused on evaluation of accuracy and performance of this method in comparison to full nested Monte Carlo simulation benchmarks.
In the first part of the ”Asset and Liability Management using LSMC” article series, we outline an ALM framework based on a replicating portfolio approach along with a suitable financial objective. This ALM framework, albeit simplified, is constructed to provide a straightforward replication of the complex interactions between assets and liabilities. Moreover, a brief introduction to the LSMC method used to generate all underlying risk factors is presented.
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