This work addresses the challenges of managing personal wealth, recognizing that traditional approaches are inadequate in assisting people to make well-informed financial decisions. Individuals with basic financial knowledge often try to manage their investments using ETFs or mutual funds but struggle to align their strategies with their specific needs, potentially exposing themselves to significant risks. On the other hand, also those relying on professional advice face dissatisfaction due to outdated methods and high fees. In summary, there is a clear demand for flexible and innovative solutions in financial management. Within the realm of financial decision-making under uncertainty, asset and liability management (ALM) emerges as a suitable framework. ALM involves strategically managing assets, taking into account both current financial goals and future obligations or liabilities. Traditional approaches frame the problem of asset and liability management for individuals (iALM) as a stochastic optimization problem, solved through dynamic stochastic programming (DSP) methods. This approach presents significant challenges in practical implementation, relying on complex algorithms and leading to extended computational and execution times. In this paper an innovative framework is proposed. While still adhering to ALM principles, the new methodology shifts to a recommendation system paradigm. Our approach is highly practical: we design an industrial process for providing advanced financial consulting. The objective is to address clients' needs by offering tangible solutions in the form of insurance, credit and investment products. The process combines traditional customer knowledge and business intelligence methods with innovative data-driven approaches that utilize machine learning. Our approach holds great value for financial institutions looking to offer personalized financial products to their clients. Its customized and practical nature guarantees heightened customer satisfaction, translating into tangible profit generation for businesses and enhanced wealth management for individuals and families.
Questo lavoro affronta le sfide legate alla gestione della ricchezza personale, riconoscendo che gli approcci tradizionali sono inadeguati nel supportare le persone a prendere decisioni finanziarie informate. Individui con conoscenze finanziarie di base spesso cercano di gestire i loro investimenti utilizzando ETF o fondi comuni, ma faticano ad allineare le strategie alle loro esigenze specifiche. D'altra parte, anche coloro che si affidano a consulenze professionali affrontano insoddisfazione a causa di metodi superati e costi di commissione elevati. In sintesi, c'è una chiara richiesta di soluzioni flessibili e innovative nella gestione finanziaria. Nel contesto delle decisioni finanziarie in un ambiente di incertezza, la gestione degli attivi e dei passivi (ALM) emerge come un approccio adeguato. L'ALM implica la gestione strategica degli asset, tenendo conto degli obiettivi finanziari e delle passività future. Gli approcci tradizionali formulano l'argomento come un problema di ottimizzazione stocastico, risolto attraverso metodi di programmazione dinamica stocastica (DSP). Questo approccio presenta ostacoli nell'implementazione pratica, basandosi su algoritmi complessi e richiedendo tempi di esecuzione prolungati. In questo articolo viene proposto un framework innovativo. Pur aderendo ai principi dell'ALM, la nuova metodologia è un sistema di raccomandazione. Il nostro approccio è altamente pratico: progettiamo un processo industriale per fornire consulenza finanziaria avanzata. L'obiettivo è rispondere alle esigenze dei clienti offrendo soluzioni sotto forma di prodotti assicurativi, creditizi e di investimento. Il processo combina la conoscenza del cliente e del business con approcci innovativi basati sui dati che utilizzano il machine learning. Il nostro approccio ha un grande valore per le istituzioni finanziarie che desiderano offrire prodotti finanziari su misura per i propri clienti. La sua natura pratica e personalizzata garantisce una maggiore soddisfazione del cliente, traducendosi in una generazione di profitti per le aziende e una migliore gestione patrimoniale per le famiglie.
Innovating Individual Asset and Liability Management with Machine Learning: A Fresh Approach
COTRONEO, ALESSIA
2022/2023
Abstract
This work addresses the challenges of managing personal wealth, recognizing that traditional approaches are inadequate in assisting people to make well-informed financial decisions. Individuals with basic financial knowledge often try to manage their investments using ETFs or mutual funds but struggle to align their strategies with their specific needs, potentially exposing themselves to significant risks. On the other hand, also those relying on professional advice face dissatisfaction due to outdated methods and high fees. In summary, there is a clear demand for flexible and innovative solutions in financial management. Within the realm of financial decision-making under uncertainty, asset and liability management (ALM) emerges as a suitable framework. ALM involves strategically managing assets, taking into account both current financial goals and future obligations or liabilities. Traditional approaches frame the problem of asset and liability management for individuals (iALM) as a stochastic optimization problem, solved through dynamic stochastic programming (DSP) methods. This approach presents significant challenges in practical implementation, relying on complex algorithms and leading to extended computational and execution times. In this paper an innovative framework is proposed. While still adhering to ALM principles, the new methodology shifts to a recommendation system paradigm. Our approach is highly practical: we design an industrial process for providing advanced financial consulting. The objective is to address clients' needs by offering tangible solutions in the form of insurance, credit and investment products. The process combines traditional customer knowledge and business intelligence methods with innovative data-driven approaches that utilize machine learning. Our approach holds great value for financial institutions looking to offer personalized financial products to their clients. Its customized and practical nature guarantees heightened customer satisfaction, translating into tangible profit generation for businesses and enhanced wealth management for individuals and families.File | Dimensione | Formato | |
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2023_12_Cotroneo_Executive Summary_02.pdf
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https://hdl.handle.net/10589/214001