How AI Changes The Risk Management Process In Organizations?

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Risk Management

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What is the Objective of Risk Management?

The main objective or goal of risk management is to identify the possible issues before they happen. Risk management includes 2 stages of the process – the first one is what risks exist in an investment and the second one is handling or managing those risks in the best way.

Risk management makes sure that an organization or industry identifies and understands the risks to which it is exposed. Risk management also guarantees that the organization creates and implements an effective system to prevent losses or reduce the impact if a loss occurs.

Why do we need Risk Management?

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Insurance companies need to manage a risk management system to recognize risk activities or events and change their claim rates before and also they can save a huge amount of money. Risk management can understand and as well as control that risk; this gives more confidence to insurance companies at the stage of decision making.

Every insurance company or organization has so many risks to manage at its organization-level including underwriting, reinsurance, operational, marketing, and liquidity risks. traditional risk management activities are outdated. Insurance companies hire few members as a risk management team; they evaluate documents after receiving loan applications but these are not that accurate results based on FICO (Fair Isaac Corporation) score.

So insurance companies may end up with more losses due to the risks they faced than desired. So insurance companies need to maintain more accurate results on the risk management system.

Nowadays so many companies are starting to adopt Machine Learning or AI-based models to find and also prevent risk more accurately.

How does an AI-based solution work in Risk Management?

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Insurance companies take more time in terms of days if they evaluate risk from new customers. And also these tasks want more risk analysts for analyzing different types of risks.

AI-Based models will help companies by analyzing already existing or past customer information and then apply it to new customers in a faster way and with accurate results.

AI-Based risk management systems or models will allow insurance companies to attempt to prepare for the unexpected by reducing risks and as well as extra costs before they happen. Because AI-Based models can avoid potential risks, minimizing their impact. This will help insurance companies in the growth of the business.

AI-based Risk management systems can provide many advantages to insurance companies or agencies.

For example

    1. Insurance companies don’t need to hire a team to identify and provide a deeper understanding of all types of risks.
    2. Risk management will help to provide insights about risks. Sometimes board members face or identity difficulties while finding risks outside of their areas of expertise and experiences.
    3. Reduce business responsibilities. Regulators and shareholders view litigation risk as a business responsibility. Reducing litigation risk upfront makes the company a more attractive investment. This will be done with risk management.
    4. Increase stability in business operations.
    5. Protects all who are involved in severe risks, etc.

AI-based Risk Management System Process

    • Data Extraction

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    • In this problem statement also we have to collect or gather information about clients or customers from insurance agencies. This information or data may be collected from databases of organizations and also documents that are submitted by clients while applying for insurance or claims. After collecting data we should extract data from all documents of individual customers. Now we have to create a dataset with all collected data from databases as well as documents.

    • Data Annotation

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    • After creating a dataset from different sources, that data will be sent to the annotation phase. In this phase, the annotator will perform 2 types of annotations on the data.

    • Sequence labeling annotation: In this type of annotation process annotator labeling one or more tags within the text. For example, they will annotate a single text or sentence or document with one or more tags such as personal_details, marital status, gender, family history, medical history, etc.
    • Text classification annotation: In this process, annotators have to annotate the whole text or document of a particular user with one tag. For example, tags related to that text leads to risk or not. Annotators annotate text with different tags like very low, low, medium-low, high, very high, medium-high, no risk.

      This stage wants annotators who are knowing insurance different risks, possibilities of risks, etc. If the annotator doesn’t know when they make mistakes that will lead to low performance of the model.

      In this problem statement also the annotation process will help the model to provide better performance. Based on annotated documents, models learn patterns to recognize risks and then manage them. Otherwise, models don’t know about customer documents like if documents have some particular features that belong to one risk. If documents have another set of features, then that belongs to another risk type.

      All these kinds of information will be learned by models while training or building AI-Based models. We add this information to our raw data or extracted data to add value to our dataset through an annotation process.

    • Data Processing

      After the annotation phase is completed we will use that annotated data for data preprocessing. Here we have to apply different types of text preprocessing techniques on annotated data. Some of the text preprocessing techniques while doing this phase is tokenization, stemming, lemmatization, removing stop words, extra spaces, text normalization techniques, etc. By the end of this stage, we will get valuable as well as qualitative data to build better models.

    • Model Development

      In the development phase of the model for risk management, we will use preprocessed text. Our model aim is to identify the risks so that we can utilize NLP-based topic modeling techniques to identify top risks. This is the statistical model to identify abstract topics from the documents. And also a discovery group of words that leads to that particular topic. For topic analysis, the most popular NLP techniques are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).

      While building this risk management there are different types of NLP techniques used like topic modeling, named entity recognition for recognizing entities like personal details, age, gender, etc. And for characterizing risk factors we will use the POS (Parts-Of-Speech) tagger. Like we will use different types of techniques in this model building.

      POS Tagging

      Part-of-speech (POS) tagging strategy is utilizing to filter the fascinating categories, semantic networks to recover semantic connections between phrases as concept instances, syntactic and semantic information to construct concept recognition heuristics applied to messages.

      Named Entity Recognition

      Entity recognition utilizes statistical modeling, neural networks, and regular expression pattern mapping with an end goal to separate and classify every entity or element. This usefulness of Natural Language Processing (NLP) is capable of recognizing named entities, for example, people’s names, cities, urban areas, nations, areas, birth dates, and others.

      Entity recognition can help proficiently recognize documents with privileged information or recognizable data, including social security and credit card numbers. Classifying these entities can reduce privacy risk and organize privilege reviews.

      Sentiment Analysis

      Sentiment analysis is the way toward extracting emotional data from various sorts of information. In risk management, sentiment analysis or opinion mining can assist organizations with seeing any abstract opinions of business, and make a technique as per risks or dangers may go over in business examination.

    • Inference and Deployment of the Model

      By using our trained risk management model insurance agencies can know different information from new customers such as identifying top risks from documents and effectiveness of each top risks and how to manage those risks, etc. nowadays due to low knowledge of teams who are working on risk management in insurance agencies, companies lose huge amounts of money.

      By building a prediction system for all of these trained models we can send requests/input through HTTP requests and get back the response for all the trained models either in JSON/XML format. Later on, by using those responses we can able to build a sophisticated web application having POS tagger visualization, showing topics through visualization, etc.

      By utilizing our trained model companies can identify risks before they become more dangerous. So companies were able to prevent risks earlier so automatically the economical growth of the companies gradually increased.