6 AI solutions every commercial bank needs

eden — Wed, 08 Jan 2020 03:35:04 +0000

DataRobot’s automated machine learning platform helps banks leverage their substantial investments in data to meet today’s challenges. By learning from their own data, banks can find and attract the best new clients, deepen existing client relationships, improve the client experience, and identify new growth opportunities while meeting regulatory requirements and fighting financial crime effectively and efficiently.

1. NLP – Natural Language Processing

This new eBook highlights practical use cases for AI in today’s investment banking market. Armed with this knowledge, investment bankers can take advantage of the enormous amount of data they generate and transform into AI-enabled enterprises.

The most important use cases of Natural Language Processing are:

Sentiment analysis aims to determine the attitude or emotional reaction of a person with respect to some topic – e.g. positive or negative attitude, anger, sarcasm. It is broadly used in customer satisfaction studies (e.g. analyzing product reviews).

2. Reinforcement learning

Download your copy to find out how AI and machine learning can help you grow your business and outperform your competition.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!

Aron Larsson

– CEO, Strategy Director

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Why today’s retail banks need AI to win

eden — Wed, 08 Jan 2020 03:33:22 +0000

Competition in retail banking may be more intense than ever as FinTechs and new market entrants fight with established players for deposits and market share. Retail banks that embrace advanced analytics and leverage their valuable data can gain a decisive competitive advantage.

1. Predicting client needs

Deeper client relationships are both more profitable and more loyal. By learning from their data, banks can identify, even anticipate, client needs that they can help with. Clients are far more likely to respond to a relevant offer and to have a favorable impression of your bank than they are if you are still sending indiscriminate offers with minuscule response rates.

The most important use cases of Natural Language Processing are:

Document Summarization is a set of methods for creating short, meaningful descriptions of long texts (i.e. documents, research papers).

2. Keeping existing customers is at least as important as finding new ones.

Banks can learn from their client interaction data to identify customers at risk of attrition and take preemptive action. Even better, good models can identify the leading causes of attrition risk so that you can make process adjustments or improvements in order to hold onto more of your most valuable clients.

3. Price optimization and lifetime value

Many banks use a score-carding process in consumer lending, determining what terms to offer if the borrower meets certain criteria. Often, these are based on risk appetite rather than any insight into price elasticity or profit margin/volume tradeoffs. If banks knew which clients were likely to be the most profitable and knew how those clients were likely to respond to price differences, then they might price more aggressively in order to land those clients.