What are the best ways to use machine learning and artificial intelligence in finance?

Según mi experiencia, la automatización de procesos mediante la adopción de la IA y strategy data va tendiendo puentes entre servicios con valor agregado que ofrecen los bancos y la percepción de la misma por parte de sus clientes o potenciales clientes. Por ejemplo, que puedas tener mejores productos por la vía online que por el canal físico es una muestra de personalización de producto y agilidad (lo cual deviene en menos costos para el banco). Ahora, la parte negativa es que si en el proceso de adopción de AI, se hace un quiebre brusco, puede generar una impacto negativo para la empresa. Prescindir en gran % de personas que atienden llamadas y reemplazarlas por AI de golpe, va a generar incomodidad en los clientes.

Machine learning enables banks to analyze customer data and behavior to offer personalized services such as tailored product recommendations, customized financial advice, and individualized pricing strategies.

Machine learning (ML) and artificial intelligence (AI) are rapidly transforming the financial landscape. In personal banking ML ML-powered chatbots such as Simone (Our NCB Chatbot) can answer questions, resolve issues, and personalize recommendations for financial products. Further applications include automating repetitive tasks like bill payments and expense categorization.

Machine learning models can assess and quantify various types of risks, including credit risk, market risk, and operational risk, by analyzing historical data and identifying risk factors. These models help financial institutions better manage their risk exposure. Fraud Detection: AI-powered fraud detection systems can analyze transaction data in real-time to detect suspicious patterns or anomalies that may indicate fraudulent activity, helping to prevent financial losses and protect customers.

AI algorithms can analyze vast amounts of data to identify and assess risks more accurately, enabling financial institutions to make informed decisions in areas like credit risk assessment, fraud detection, and portfolio management.

The risk management applications for ML and AI are more closely related to helping individuals improve their fraud detection and credit risk assessment functions. Integrating risk assessment software into investor risk assessment is one example. ML can analyze a borrower's financial health beyond traditional credit scores, enabling more informed lending decisions and improved risk mitigation. As well as ML algorithms can also be used to analyze transaction data to identify out-of-pattern transactions in real time which can reduce financial losses.

I'm probably not the biggest advocate for actual AI trading, but rather AI-enabled decision-making when it comes to trading. AI-powered algorithms can analyze market data, identify trends, and make recommendations for traders saving crucial time. AI can sift through massive datasets, uncovering valuable insights and trends that might be missed by human analysts, and aiding in better investment decisions.

Machine learning algorithms can analyze market trends, news sentiment, and historical data to identify patterns and make predictions, aiding traders and investors in making more informed decisions and optimizing investment strategies.

Una utilidad importante que se dio en pandemia fue el uso y masificación de las billeteras digitales. En Perú se tiene un serio problema con respecto a la bancarización producto de la informalidad. A raíz de la pandemia y en vista que nadie quería usar dinero físico por temor al contagio, se masificó el uso de Yape o Plin, billeteras digitales más conocidas en ese momento, las cuales podrías abrir sólo con un documento de identidad, viéndose beneficiadas negocios de todo tipo como mercados, servicios de taxi, etc. Si bien es cierto, no es una bancarización del todo, es un primer paso para incluir a las personas en el sistema financiero, que ya sea por miedo, desconfianza o desconocimiento se encuentran al margen del sistema.

AI-powered technologies can help expand access to financial services for underserved populations by automating processes, reducing costs, and mitigating risks associated with serving marginalized communities, thus promoting financial inclusion.

Small companies also need financial inclusion. They don't afford a CFO or a finance professional. ML and IA can help all the small companies providing tools to make informed decisions in their businesses. Making more successful, profitable and sustainable small companies in the future.

Machine learning and AI foster innovation in finance by enabling the development of new products and services, such as algorithmic trading systems, robo-advisors, peer-to-peer lending platforms, and blockchain-based solutions, driving industry growth and competitiveness.

La innovación financiera está muy relacionada a las Fintech, negocios disruptivos en servicios financieros, los cuales buscan brindar productos atomizados y personalizados (muchos de ellos en base a data e AI). Si bien es cierto existe un boom de las Startup financieras, debe existir mucho control en la información, tanto para las empresas que las administran, como para las Instituciones que velan por la seguridad de la información de las personas, ya que puede devenir en problemas de seguridad informática y/o ciudadana. La labor de compliance es vital en este escenario.

AI can provide financial actors with investing strategies based on the past but also predictions that take into consideration several parameters democratising investing in a way for people without the background to research for investing strategies.

Considero que la data es el nuevo oro del siglo XXI, pero con ello lleva consigo mucha responsabilidad. Adopción de Leyes de protección de datos personales y su respectivo cumplimiento sería el mundo ideal. Recibir comunicaciones o llamadas de empresas ofreciendo sus productos, en las cuales tu no tienes intención de compra genera incomodidad (peor aun con llamadas constantes). Es por eso, para cumplir los temas éticos con respecto a datos personales e innovación financiera, se requiere de Instituciones sólidas que nos garanticen seguridad a los clientes.

While AI offers significant opportunities in finance, it also raises ethical concerns related to data privacy, algorithmic bias, transparency, and accountability. Addressing these challenges is crucial to ensuring responsible and equitable use of AI technologies in finance.

Other considerations include regulatory compliance, cybersecurity risks, talent acquisition and training, partnerships with fintech startups, and the need for ongoing research and development to keep pace with technological advancements and industry trends.

Leveraging generative AI for business can add efficiency to operations. AI can analyze far more data than a human in far less time. It can also offer input to help a human better analyze financial data.